While very few details have come out, Reuters reports that new proposed congressional bills on self-driving cars will reverse many of the provisions I critiqued in the NHTSA regulations last year.

One big change is a reversal of the new idea of pre-market regulation. Today, new car technologies are not regulated before they are deployed, but NHTSA proposed giving itself the power to regulate technologies even before they exist. Currently, most car technologies like adaptive cruise control, autopilots, forward collision avoidance, lane keeping and the like remain unregulated after a decade or more of deployment with few, if any, problems.

This is important because the old doctrine of “we don’t regulate until we see a problem the industry won’t fix on its own” is a much better one for innovation, and the speed of innovation is key in deciding which countries and companies lead this technology. The opposite approach of “we try to imagine what might go wrong and ban it ahead of time” may seem safer, but it’s definitely an impediment to innovation and may actually result in far more deaths through the delay of life-saving technologies.

Harder to judge is the preemption of state rules. While states are also attempting to pre-regulate, having a laboratory of 50 different competing states can also be good for innovation on the legal side. There is not one answer, and while it’s more complex to deal with 50 sets of regulations instead of one, it’s not that much more complex.

One of the few interesting and good ideas in the NHTSA regs may also vanish. NHTSA wanted all vendors to make available all sensor logs from all incidents. As I predicted, companies pushed back on this — their testing logs and the resulting test suites are very important competitive assets. The company with the best test suite is the furthest on the path to the safety needed for deployment. On the other hand, sharing this data would let everybody get further on that path, faster.

There has been lots of other news during the long road-trip I am on in Europe. This includes more entrants in the race, the retirement of Google’s 3rd generation “koala” car, more at Uber. I will report from the Autonomous Car Testing and Development conference in Stuttgart starting Tuesday.

First ride: Encountering a school bus on real city streets in Austin, Texas. Credit: Waymo/Google

Recently we’ve seen a series of startups arise hoping to make robocars with just computer vision, along with radar. That includes recently unstealthed AutoX, the off-again, on again efforts of comma.ai and at the non-startup end, the dedication of Tesla to not use LIDAR because it wants to sell cars today before LIDARs can be bought at automotive quantities and prices.

Their optimism is based on the huge progress being made in the use of machine learning, most notably convolutional neural networks, at solving the problems of computer vision. Milestones are dropping quickly in AI and particularly pattern matching and computer vision. (The CNNs can also be applied to radar and LIDAR data.)

There are reasons pushing some teams this way. First of all, the big boys, including Google, already have made tons of progress with LIDAR. There right niche for a startup can be the place that the big boys are ignoring. It might not work, but if it does, the payoff is huge. I fully understand the VCs investing in companies of this sort, that’s how VCs work. There is also the cost, and for Tesla and some others, the non-availability of LIDAR. The highest capability LIDARs today come from Velodyne, but they are expensive and in short supply — they can’t make them to keep up with the demand just from research teams!

Note, for more detailed analysis on this, read my article on cameras vs. lasers.

For the three key technologies, these trends seem assured:

1. LIDAR will improve price/performance, eventually costing just hundreds of dollars for high-resolution units, and less for low-res units.
2. Computer vision will improve until it reaches the needed levels of reliability, and the high-end processors for it will drop in cost and electrical power requirements.
3. Radar will drop in cost to tens of dollars and software to analyse radar returns will improve.

In addition, there are some more speculative technologies whose trends are harder to predict, such as long-range LWIR LIDAR, new types of radar, and even a claimed lidar alternative that treats the photons like radio waves.

These trends are very likely. As a result, the likely winner continues to be a combination of all these technologies, and the question becomes which combination.

LIDAR’s problem is that it’s low resolution, medium in range and expensive today. Computer Vision (CV)’s problem is that it’s insufficiently reliable, depends on external lighting and needs expensive computers today. Radar’s problem is super low resolution.

Option one — high-end LIDAR with computer vision assist

High end LIDARs, like the 32 and 64 laser units favoured by the vast majority of teams, are extremely reliable at detecting potential obstacles on the road. They never fail (within their range) to differentiate something on the road from the background. But they often can’t tell you just what it is, especially at a distance. It won’t know a car from a pickup truck or 2 pedestrians from 3. It won’t read facial expressions or body language. It can read signs but only when they are close. It can’t see colours, such as traffic signals.

The fusion of the depth map of LIDAR with the scene understanding of neural net based vision systems is powerful. The LIDAR can pull the pedestrian image away from the background, and then make it much easier for the computer vision to reliably figure out what it is. The CV is not 100% reliable, but it doesn’t have to be. Instead, it can ideally just improve the result. LIDAR alone is good enough if you take the very simple approach of “If there’s something in the way, don’t hit it.” But that’s a pretty primitive result that may brake too much for things you should not brake for.

Consider a bird on the road or a blowing trash bag. It’s a lot harder for the LIDAR system to reliably identify those things. On the other hand, the visions systems will do a very good job at recognizing the birds. A vision system that makes errors 1 time every 10,000 is not adequate for driving. That’s too high an error rate as you encounter thousands of obstacles every hour. But missing 1 bird out of 10,000 means that you brake unnecessarily for a bird perhaps once every year or two, which is quite acceptable.

Option two — lower end LIDAR with more dependence on vision

Low end lidars, with just 4 or so scanning planes, cost a lot less. Today’s LIDAR designs basically need to have an independent laser, lens and sensor for each plane, and so the more planes, the more cost. But that’s not enough to identify a lot of objects, and will be pretty deficient on things low to the ground or high up, or very small objects.

The interesting question is, can the flaws of current computer vision systems be made up for by a lower-end, lower cost LIDAR. Those flaws, of course, include not always discerning things in their field. They also include needing illumination at night. This is a particular issue when you want a 360-degree view — one can project headlights forward and see as far as they see, but you can’t project headlights backwards or to the side without distracting drivers.

It’s possible one could use infrared headlights in the other directions (or forward for that matter.) After all, the LIDAR sends out infrared laser beams. There are eye safety limits (your iris does not contract and you don’t blink to IR light) but the heat output is also not very high.

Once again, the low end lidar will eliminate most of the highly feared false negatives (when the sensor doesn’t see something that’s there) but may generate additional false positives (ghosts that make the vehicle brake for nothing.) False negatives are almost entirely unacceptable. False positives can be tolerated but if there are too many, the system does not satisfy the customer.

This option is cheaper but still demands computer vision even better than we have today. But not much better, which makes it interesting.

Other options

Tesla has said they are researching what they can do with radar to supplement cameras. Radar is good for obstacles in front of you, especially moving ones. Better radar is coming that does better with stationary objects and pulls out more resolution. Advanced tricks (including with neural networks) can look at radar signals over time to identify things like walking pedestrians.

Radar sees cars very well (especially licence plates) but is not great on pedestrians. On the other hand, for close objects like pedestrians, stereo vision can help the computer vision systems a lot. You mostly need long range for higher speeds, such as the highways, where vehicles are your only concern.

Who wins?

Cost will eventually be a driver of robocar choices, but not today. Today, safety is the only driver. Get it safe, before your competitors do, at almost any cost. Later make it cheap. That’s why most teams have chosen the use of higher end LIDAR and are supplementing in with vision.

There is an easy mistake to make, though, and sometimes the press and perhaps some teams are making it. It’s “easy” on the grand scale to make a car that can do basic driving and have a nice demo. You can do it with just LIDAR or just vision. The hard part is the last 1%, which takes 99% of the time, if not more. Google had a car drive 1,000 miles of different roads and 100,000 total roads in the first 2 years of their project back in 2010, and even in 2017 with by far the largest and most skilled team, they do not feel their car is ready. It gets easier every day, as tech advances, to get the demo working, but that should not be mistaken for the real success that is required.

This post originally appeared on robocars.com.

California published its summary of all the reports submitted by vendors testing robocars in the state. You can read the individual reports. They are interesting, but several other outlines have created summaries of the reports calculating things like the number of interventions per mile. On these numbers, Google’s lead is extreme. Of over 600,000 autonomous miles driven by the various teams, Google/Waymo was 97% of them — in other words, 30 times as much as everybody else put together.

Beyond that, their rate of miles between disengagements (around 5,000 — a 4x improvement over 2015) is one or two orders of magnitude better than the others, and in fact for most of the others, they have so few miles that you can’t even produce a meaningful number. Only Cruise, Nissan and Delphi can claim enough miles to really tell.

Tesla is a notable entry. In 2015 they reported driving zero miles, and in 2016 they did report a very small number of miles with tons of disengagements from software failures (one very 3 miles.) That’s because Tesla’s autopilot is not a robocar system, and so miles driven by it are not counted. Tesla’s numbers must come from small scale tests of a more experimental vehicle. This is very much not in line with Tesla’s claim that it will release full autonomy features for their cars fairly soon, and that they already have all the hardware needed for that to happen.

Unfortunately, you can’t easily compare these numbers:

• Some companies are doing most of their testing on test tracks, and they do not need to report what happens there.
• Companies have taken different interpretations of what needs to be reported. Most of Cruise’s disengagements are listed as “planned” but in theory those should not be listed in these reports. But they also don’t list the unplanned ones which should be there.
• Delphi lists real causes and Nissan is very detailed as well. Others are less so.
• Many teams test outside California, or even do most of their testing there. Waymo/Google actually tests a bunch outside California, making their numbers even bigger.
• Cars drive all sorts of different roads. Urban streets with pedestrians are much harder than highway miles. The reports do list something about conditions but it takes a lot to compare apples to apples. (Apple is not one of the companies filing a report, BTW.)

One complication is that typically safety drivers are told to disengage if they have any doubts. It thus varies from driver to driver and company to company what “doubts” are and how to deal with them.

Google has said their approach is to test any disengagement in simulator, to find out what probably would have happened if the driver did not disengage. If there would have been a “contact” (accident) then Google considers that a real incident and those are rarer than is reported here. Many of the disengagements are when software detects faults with software or sensors. There, we do indeed have a problem, but like human beings who zone out, not all such failures will cause accidents or even safety issues. You want to get rid of all of them, to be sure, but if you are are trying to compare the safety of the systems to humans, it’s not easy to do.

It’s hard to figure out a good way to get comparable numbers from all teams. The new federal guidelines, while mostly terrible, contain an interesting rule that teams must provide their sensor logs for any incident. This will allow independent parties to compare incidents in a meaningful way, and possibly even run them all in simulator at some level.

It would be worthwhile for every team to be required to report incidents that would have caused accidents. That requires a good simulator, however, and it’s hard for the law to demand this of everybody.

Professor Daniela Rus stands with autonomous SMART vehicles on the MIT campus. Photo: SMART

By: Catherine Marguerite | Singapore-MIT Alliance for Research and Technology

Daniela Rus loves Singapore. As the MIT professor sits down in her Frank Gehry-designed office in Cambridge, Massachusetts, to talk about her research conducted in Singapore, her face starts to relax in a big smile.

Her story with Singapore started in the summer of 2010, when she made her first visit to one of the most futuristic and forward-looking cities in the world. “It was love at first sight,” says the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and the director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). That summer, she came to Singapore to join the Singapore-MIT Alliance for Research and Technology (SMART) as the first principal investigator in residence for the Future of Urban Mobility Research Program.

“In 2010, nobody was talking about autonomous driving. We were pioneers in developing and deploying the first mobility on demand for people with self-driving golf buggies,” says Rus. “And look where we stand today! Every single car maker is investing millions of dollars to advance autonomous driving. Singapore did not hesitate to provide us, at an early stage, with all the financial, logistical, and transportation resources to facilitate our work.”

Since her first visit, Rus has returned each year to follow up on the research, and has been involved in leading revolutionary projects for the future of urban mobility. “Our team worked tremendously hard on self-driving technologies, and we are now presenting a wide range of different devices that allow autonomous and secure mobility,” she says. “Our objective today is to make taking a driverless car for a spin as easy as programming a smartphone. A simple interaction between the human and machine will provide a transportation butler.”

The first mobility devices her team worked on were self-driving golf buggies. Two years ago, these buggies advanced to a point where the group decided to open them to the public in a trial that lasted one week at the Chinese Gardens, an idea facilitated by Singapore’s Land and Transportation Agency (LTA). Over the course of a week, more than 500 people booked rides from the comfort of their homes and came to the Chinese Gardens at the designated time and spot to experience mobility-on-demand with robots.

The test was conducted around winding paths trafficked by pedestrians, bicyclists, and the occasional monitor lizard. The experiments also tested an online booking system that enabled visitors to schedule pickups and drop-offs around the garden, automatically routing and redeploying the vehicles to accommodate all the requests. The public’s response was joyful and positive, and this brought the team renewed enthusiasm to take the technology to the next level.

Since the Chinese Gardens public trial, the autonomous car group has introduced a few other self-driving vehicles: a self-driving city car, and two personal mobility robots, a self-driving scooter and a self-driving wheelchair. Each of these vehicles was created in three phases: In the first phase, the vehicle was converted to drive-by-wire control, which allows a computer to control acceleration, braking, and steering of the car. In the second phase, the vehicle drives on each of the pathways in its operation environment and makes a map using features detected by the sensors. In the third phase, the vehicle uses the map to compute a path from the customer’s pick-up point to the customer’s drop-off point and proceeds to drive along the path, localizing continuously and avoiding any other cars, people, and unexpected obstacles. The devices also used traffic data from LTA to model traffic patterns and to study the benefits of ride-sharing systems.

Last April, the team conducted a new test with the public at MIT. This time, they deployed a self-driving scooter that allowed users to use the same autonomy system indoors as well as outdoors. The trial included autonomous rides in MIT’s Infinite Corridor. A significant challenge in this type of space is localization, or accurately knowing the location of the robot in a long and plain corridor that does not have many distinctive features. The system proved to work very well in this type of environment, and the trial completed the demonstration of a comprehensive uniform autonomous mobility system.

“One can easily see the usefulness of such a system between self-driving city cars, golf buggies, and scooters,” Rus says. “A mobility-impaired user could, for example, use a self-driving scooter to get down the hall and through the lobby of an apartment building, take a self-driving golf buggy across the building’s parking lot, and pick up an autonomous car on the public roads to go to a similarly equipped amusement park or shopping centre.”

Daniela Rus, a Class of 2002 MacArthur Fellow and member of the USA National Academy of Engineering, knows that each successful step into urban mobility will bring a positive contribution of artificial intelligence to the public. According to the World Health Organization, 3,400 people die each day in the world from traffic-related accidents.

“It is a new space race,” she says, convinced that autonomy is part of the solution to safe transportation. Daniela Rus will continue visiting her beloved Singapore, where she particularly enjoys the food, the beautiful flowers, the kindness of its people, and the smartness of its youth. “Singapore is definitely a model in many fields,” she concludes.

Tesla Model S autopilot-software. Source: Tesla

NHTSA released the report from their Office of Defects Investigation on the fatal Tesla crash in Florida last spring. It’s a report that is surprisingly favorable to Tesla. So much so that even I am surprised.

While I did not think Tesla would be found defective, this report seems to come from a different agency than the one that recently warned comma.ai that:

It is insufficient to assert, as you do, that the product does not remove any of the driver’s responsibilities” and “there is a high likelihood that some drivers will use your product in a manner that exceeds its intended purpose.

The ODI report rules that Tesla properly considered driver distraction risks in its design of the product. It goes even further, noting that drivers using Tesla Autopilot (including those monitoring it properly and those who did not) still had a decently lower accident rate for mile than drivers of ordinary cars without autopilot. In other words, while the Autopilot without supervision is not good enough to drive on its own, the Autopilot even with occasionally lapsed supervision that is known to happen is still overall a safer system than not having the Autopilot at all.

This will provide powerful support for companies developing autopilot style systems, and companies designing robocars who wish to use customer supervised driving as a means to build up test miles and verification data. They are not putting their customers at risk as long as they do it as well as Tesla. This is interesting (and the report notes that evaluation of Autopilot distraction is not a settled question) because it seems probable that people using the Autopilot and ignoring the road to do e-Mail or watch movies are not safer than regular drivers. But the overall collection of distracted and watchful drivers is still a win.

This might change as companies introduce technologies which watch drivers and keep them out of the more dangerous inattentive style of use. As the autopilots get better, it will become more and more tempting, after all.

Tesla stock did not seem to be moved by this report. But it was also not moved by the accident or other investigations — it actually went on a broadly upward course for 2 months following the announcement of the fatality.

The ODI’s job is to judge if a vehicle is defective. That is different from saying it’s not perfect. Perfection is not expected, especially from ADAS and similar systems. The discussion about the finer points of whether drivers might over-trust the system are not firmly settled here. That can still be true without the car being defective and failing to perform as designed or being designed negligently.

Source: Waymo

Google’s car project (known as “Chauffeur”) kickstarted the entire robocar revolution, and Google has put in more work, for longer, than anybody. The car was also the first project of what became Google “X” (or just “X” today under Alphabet. Inside X, a lab devoted to big audacious “moonshot” projects that affect the physical world as well as the digital, they have promoted the idea that projects should eventually “graduate,” moving from being research to real commercial efforts.

Alphabet has announced that the project will be its own subsidiary company with the new name “Waymo.” The name is not the news; what’s important is the move away from being a unit of a mega-company like Google or Alphabet. The freedoms to act that come with being a start-up (though a fairly large and well funded one) are greater than units in large corporations have. Contrast what Uber was able to do, skirting and even violating the law until it got the law changed, with what big corporations need to do.

Google also released information about how in 2015 they took Steve Mahan — the blind man who was also the first non-employee to try out a car for running errands — for the first non-employee (blind or otherwise) fully self-driving ride on public streets, in a vehicle with no steering wheel and no backup safety driver in the vehicle. (This may be an effort to counter large amount of press about public ride offerings by nuTonomy in Singapore and Uber in Pittsburgh, as well as truck deliveries by Uber/Otto in 2016.)

It took Google/Alphabet 6 years to let somebody ride on public streets in part because it is a big company. It’s an interesting contrast with how Otto did a demonstration video after just a few months of life of a truck driving a Nevada highway with nobody behind the wheel (but Otto employees inside and around it.) That’s the sort of radical step that startups do.

Waymo declared their next goal is to “let people use our vehicles to do everyday things like run errands, commute to work, or get safely home after a night on the town.” This is the brass ring, a “Mobility on Demand” service, which is able to pick people up (ie. run unmanned) and even carry a drunk person.

The last point is important. To carry a drunk is a particular challenge. In terms of improving road safety, it’s one of the most worthwhile things we could do with self-driving cars since drunks have so many of the accidents. To carry a drunk, you can’t let the human take control even if they want to. Unlike unmanned operation, you must travel at the speed impatient humans demand, and you must protect the precious cargo. To make things worse, in some legal jurisdictions, they still want to consider the person inside the car the “driver,” which could mean that since the “driver” is impaired, operation is illegal.

Waymo as leader

The importance of this project is hard to overstate. While most car companies had small backburner projects related to self-driving going back many years, and a number of worthwhile research milestones were conquered in the 90s and even earlier, the Google/Waymo project, which sprang from the Darpa Grand Challenge, energized everybody. Tiny projects at car companies all got internal funding because car companies couldn’t tolerate the press and the world thinking and writing that true future of the car was coming from a non-car company, a search engine company. Now the car companies have divisions with thousands of engineers and it’s thanks to Google. The Google/Waymo team was accomplishing tasks 5 years ago that most projects are only now just getting to, especially in non-highway driving. They were rejecting avenues (like driving with a human on standby ready to take the wheel on short notice) in 2013 that many projects are still trying to figure out.

Indeed, even in 2010, when I first joined the project and it had just over a dozen people, it had already accomplished more complex tasks that most projects, even the Tesla Autopilot that some people think is in the lead, have yet to accomplish.

Let’s see where Waymo goes.

Oxbotica testing Selenium. Credit: Oxbotica/YouTube

Oxbotica, a UK technology company with a focus on mobile robotics and driverless vehicles, has created Selenium, an autonomous software system acting similar to a ‘brain’ for a vehicle. Selenium can work in pedestrianised environments as well as roads and motorways, and is not reliant on GPS to operate – meaning it can transition between indoor and outdoor settings, overground or underground. The system has been developed to be “vehicle agnostic” and can be applied to cars, self-driving pods (e.g. for campuses and airports), and warehouse truck fleets.

The software developed by Oxbotica’s team of scientists, mathematicians and engineers gives any vehicle an awareness of where it is, what surrounds it, and how it should move to complete a task.

Selenium’s system uses patented algorithms giving vehicles a next generation level of intelligence to autonomously perform a range of mobility tasks, including motion control, braking, calibration, navigation, static and dynamic obstacle detection. One of the main focusses at the moment is low-speed autonomous transport, using low-cost vision sensors, explains Dirk Gorissen, Senior Engineer at Oxbotica.

“While you are travelling at a fairly low speed, it is a much more unstructured and unpredictable environment than, say, a highway. There is a lot of talk of level 4 or even 5 autonomy but, to our knowledge, nobody has reached this reliably yet. Even Google with their millions of autonomously driven miles still needs a safety driver. There is a lot of work to get there, and we have our own roadmaps in place that will enable our customers to be class leading,” he said.

https://www.youtube.com/watch?v=cfTst2AV6M8

“Robotics outside of simulation is difficult and getting fully reliable solutions that work anytime and any place are where many others have failed,” said Gorissen. “Many components need to work together seamlessly in order to create a safe, reliable system. Particularly in the context of autonomous vehicles where you have to deal with human behaviour from other drivers and road users. In our launch projects in Greenwich and Milton Keynes, we are working in pedestrianised areas and working amongst pedestrians, cyclists, cats and dogs, as well as, regular road users.”

https://www.youtube.com/watch?v=0-Io1JMalSo

Selenium is set to be deployed at a series of autonomy trials where Oxbotica is the sole supplier of autonomy software, including the £8 million GATEway project in Greenwich and the LUTZ Pathfinder self-driving pod project in Milton Keynes. The company is also working with manufacturers in a broad spectrum of mobile autonomy domains, driverless cars.

“A key strength of the Oxford heritage is our focus on systems that learn from experience. The more often a vehicle traverses a given route the more it learns about its environment and how it changes over time (rain, fog, snow, road works, etc.),” said Gorissen. “We don’t believe in building a single all-encompassing model of the world or that autonomy is a binary on/off switch that is either active all the time or never. Rather we believe in a system that will offer autonomy as and when it is confident enough to do so. Scaling can then be achieved by sharing experiences of the world between vehicles.”

“We are very carefully focused – intentionally – on the things that we do well. For the applications and domains we have set our sights on we are fully on track and you will be able to see our Selenium autonomous control system running public demos of pods around Greenwich in East London from early 2017,” he said.

Oxbotica is a spin-off from Oxford University’s Mobile Robotics Group.

If you liked this article, you may also want to read:

• What to expect from autonomous cars
• Autonomous truck platoons travel across Europe
• How much can customers test robocars?
• The inevitability of the global march toward self-driving vehicles
• Two New Year’s resolutions for developers of automated vehicles
• comma.ai’s neural network car and new technology in robocars

See all the latest robotics news on Robohub, or sign up for our weekly newsletter

Some people have wondered about my forecast in the spreadsheet on robotaxi economics about the very low parking costs I have predicted. I wrote about most of the reasons for this in my 2007 essay on Robocar Parking, but let me expand and add some modern notes here.

The glut of parking

Today, researchers estimate there are between 3 and 8 parking spots for every car in the USA. The number 8 includes lots of barely used parking (all the shoulders of all the rural roads, for example) but the value of 3 is not unreasonable. Almost all working cars have a spot at their home base, and a spot at their common destination (the workplace.) There are then lots of other places (streets, retail lots, etc.) to find that 3rd spot. It’s probably an underestimate.

We can’t use all of these at once, but we’re going to get a great deal more efficient at it. Today, people must park within a short walk of their destination. Nobody wants to park a mile away. Parking lots, however, need to be sized for peak demand. Shopping malls are surrounded by parking that is only ever used during the Christmas shopping season. Robocars will “load balance” so that if one lot is full, a spot in an empty lot too far away is just fine.

Small size and Valet Density

When robocars need to park, they’ll do it like the best parking valets you’ve ever seen. They don’t even need to leave space for the valet to open the door to get out. (The best ones get close by getting out the window!) Because the cars can move in concert, a car at the back can get out almost as quickly as one at the front. No fancy communications network is needed; all you need is a simple rule that if you boxed somebody in, and they turn on their lights and move an inch towards you, you move an inch yourself (and so on with those who boxed you in) to clear a path. Already, you’ve got 1.5x to 2x the density of an ordinary lot.

I forecast that many robotaxis will be small, meant for 1-2 people. A car like that, 4’ by 12’ would occupy under 50 square feet of space. Today’s parking lots tend to allocate about 300 square feet per car. With these small cars you’re talking 4 to 6 times as many cars in the same space. You do need some spare space for moving around, but less than humans need.

When we’re talking about robotaxis, we’re talking about sharing. Much of the time robotaxis won’t park at all, they will be off to pick up their next passenger. A smaller fraction of them will be waiting/parked at any given time. My conservative prediction is that one robotaxi will replace 4 cars (some estimate up to 10 but they’re overdoing it.) So at a rough guess we replace 1,000 cars, 900 of which are parked, with 250 cars, only 150 of which are parked at slow times. (Almost none are parked during the busy times.)

Many more spaces available for use

Robocars don’t park, they “stand.” Which means we can let them wait all sorts of places we don’t let you park. In front of hydrants. In front of driveways. In driveways. A car in front of a hydrant will be gone at the first notification of a fire or sound of a siren. A car in front of your driveway will be gone the minute your garage opens or, if your phone signals your approach, before you get close to your house. Ideally, you won’t even know its there. You can also explicitly rent out your driveway space for money if you wish it. You could rent your garage too, but the rate will be so low you will prefer to use it to add a new room to your house unless you still own a car.

In addition, at off-peak times (when less road capacity is needed) robocars can double park or triple park along the sides of roads. (Human cars would need to use only the curb spots, but the moment they put on their turn signal, a hole can clear through the robocars to let them out.)

So if we consider just these numbers — only 1/6 of the time spent parking and either 4 times the density in parking lots or 2-3 times the volume of non-lot parking (due to the 2 spots per car and loads of extra spots) we’re talking about a huge, massive, whopping glut of parking. Such a large glut that in time, a lot of this parking space will be converted to other uses, in time reducing the glut.

Ability to move in response to demand

To add to this glut, robocars will be the best parking customers you could ever imagine. If you own a parking lot, you will have sold the space at the back or top of your lot to the robocars — they will park in the unpopular more remote sections for a discount. The human driver customers will take those spots by the entrance. As your lot fills up, you can ask the robocars to leave, or pay more. If a high paying human driver appears at the entrance, you can tell the robocars you want their space, and off they will go to make room. Or they can look around on the market and discover they should just pay you more to keep the space. The lot owner is always making the most they can.

If robocars are electric, they will also be excellent visitors by making little noise and not emitting soot to dirty your walls. They will leave a tiny amount of rubber and that’s about it.

The “spot” market

All of this will be driven by what I give the ironic name of the “spot” market in parking. Such markets are already being built by start-ups for human drivers. In this market, space in lots will be offered and bid for like any other market. Duration will be negotiated, too. Cars will evaluate potential waiting places based on price and the time it will take to get there and park, as well as the time to get to their likely next pickup. A privately owned car might drive a few miles to a super cheap lot to wait 7 hours, but when it’s closer to quitting time, pay a premium (in competition with many others of course) to be close to their master.

Ideally, driveway owners and cities would put all the spots they control into the spot market too. Free parking may vanish and be replaced by very cheap parking. Human drivers with smartphones will also buy their parking in the spot market, ideally. In some cases, free parking will appear there with conditions. A retailer may offer free parking to human drivers (who probably want to shop) or frictionless validation systems could be built. However, parking may get so cheap that all of this becomes unimportant.

The zero incremental cost

Parking is perhaps the lowest incremental cost product there is. If you own a parking spot, your costs don’t change if it’s occupied or not — not in a world of automatic electronic payments. If you leave a spot empty when somebody would pay for it — pay almost anything — you are leaving money on the table. There are many products roughly of this class, but few as strongly in it as a parking spot. Especially when we’re talking about an electric robocar that drips no fluids, emits no soot, pays online, and will leave if you have a higher paying customer. There’s not even opportunity cost in that case. About the only cost is extremely minor pavement wear, deposit of rubber and traffic going in and out. Perhaps a slight increment in security (though cars will monitor their own.)

What happens if we have a glut of spaces, and leaving a space empty leaves money on the table? It means the base price of parking is very, very cheap. People will accept offers of 10 cents/hour because they will get nothing if they refuse.

In some markets, players band together (though this is technically illegal) and put an artificial floor on prices. This could happen here — it could even be made legal to “save the industry” which will indeed be in trouble. But it only takes one person with an empty lot within a few miles to break ranks. What cities do with their on-street parking will also have a big effect.

Street parking is, as noted a special case. At peak times, most cars and all robotaxis are in service, and park/stand only briefly. On high-demand routes, street parking will be forbidden, and robotaxis will hear and obey an order to leave. Off-peak, more cars need to park, but road capacity needs are also less, and cities can open street parking. They can even open 2 lanes per side of double-parked street parking on the wider streets of lower usage. In addition, any driveways which held a car that went out on commute become available, as do the spaces in front of all driveways. Cities will face pressure from parking lots not to drop prices too low on this parking. Street parking will be more valuable, because it takes zero time to get in and out of a street space compared to a parking lot (especially a multi-floor one.)

At night, there is tons of long term capacity; all the office and closed retail lots, the sides of most streets, and the driveways of homes who have given up car ownership. The owned cars will be living there (after visiting the charging station for a few hours.)

Parking and charging

When electric robocars need to charge, they can travel to the electricity. You don’t need to put charging stations at every home and in every parking lot, though you will have some there. The best places for charging stations will be power grid transformer substations, where many megawatts can be tapped at low infrastructure cost. This may be some of the most expensive parking to be found and lots around these substations will be the few to go up in value.

People already make robotic fueling stations for gasoline, so robotic plug-in seems pretty likely here as well. You will pay for the electricity, but the car will also pay for time taking up a spot so cars will want to charge fast. Fortunately charging is getting pretty fast, and the 10kwh battery of a single person robotaxi won’t take long. Today, there is a trade-off: The faster you charge, the more risk you have of reducing the life of the battery, so there will be a sweet spot charge rate for cars not in a hurry. Robocars will rarely be in a hurry.

When the glut ends

At a rate of only dimes per hour, nobody is likely to build more parking, and many people will want to convert their parking lots to new purposes. That will take time — in fact in many cities the zoning laws actually demand that buildings have adequate parking. Open parking lots are easy to repurpose, but big parking structures might be difficult to convert and require demolition. Some parking structures are in basements or make up the central core of buildings, spaces without light that are not useful for offices.

As the spaces disappear, the prices will go up, but at the same time, there will be more and more robocars and robotaxis. Outside of the downtown cores, land is not free but bare paved land is actually not that expensive. An acre of land can hold over 700 single person robotaxis parked densely. Even at $100,000 per acre that’s somewhat tolerable, and in most places land is cheaper than that. If only holding lightweight vehicles, you can pave at a much lower cost, too.

In super-dense downtown cores, parking will still cost money, but cars will have the option to get a couple of miles out of town. For robotaxi users, if there is not enough capacity to bring in all the cars at 5pm for their passengers, vans and buses could arise to take people in bulk in roughly the right direction to outside the congestion zone, where their personal ride will be waiting right where the van stops.

So will you circle for parking?

I have frequently seen people say they imagine robocar owners would tell their cars to just drive around rather than pay for parking. That’s never been very likely. Today’s cars cost about $20/hour to run, and that’s more than today’s parking. Tomorrow’s cars may cost as little as $2-3/hour to run but that will be more than tomorrow’s parking. (It’s also unlikely you would send you car 15 miles to your home to wait. The parking will cost less, and robotaxi service for those still at home will be cheap as well.)

It is my hope that cities will take the opportunity to convert some of the no longer needed parking lots to interesting new purposes, including of course parks of the natural kind. Yes, some will turn into condo towers and office blocks, but it’s a rare opportunity to gain a bonanza of land in the cities.

Michigan’s Senate are reviewing several bills related to automated driving. SB995, 996, 997, and 998 are now out of committee, and SB 927 and 928 are not far behind. These bills seem to be a mixed bag. Critically, they are in desperate need of clarification followed by thoughtful discussion.

Editor’s note: Please note that this article refers to the versions that came out of the senate before moving to the house, where they have been slightly modified.

What’s good

SB 995 would repeal the state’s express ban on automated driving generally — a three-year-old anachronism that has frustrated officials promoting Michigan to developers of automated driving systems and that could eventually frustrate early efforts to actually deploy those systems. At a minimum, this would return Michigan law to flexible ambiguity on the question of the legality of automated driving in general. The bill probably goes even further by expressly authorizing automated driving: It provides that “[a]n automated motor vehicle may be operated on a street or highway on this state,” and the summary of the bill as reported from committee similarly concludes that SB 995 would “[a]llow an automated motor vehicle to be operated on a street or highway in Michigan.” (This provision is somewhat confusing because it would be added to an existing statutory section that currently addresses only research and testing and because it would seem to subvert many proposed restrictions on research tests and “on-demand automated motor vehicle networks.”) Regardless, this bill would also exempt groups of closely spaced and tightly coordinated vehicles from certain following-distance requirements that are incompatible with platooning. Furthermore, by using key definitions from SAE J3016, SB 995 would also help to align legal language with credible technical language.

What’s weird

SB 995 and 996 may or may not give recognized manufacturers of motor vehicles a special driverless taxi privilege, and they may or may not disadvantage companies that cannot partner with such a manufacturer — but they definitely do add unnecessary confusion. Together, these two bills expressly authorize “on-demand automated motor vehicle networks” that involve a recognized motor vehicle manufacturer in some capacity. Under the bills as originally drafted, only these manufacturers would have been eligible to “participate” in those networks. This would have meant that General Motors could run an “on-demand automated motor vehicle network” while Google and Uber could not. Although nothing in the original bills would have explicitly — or even, at least arguably, implicitly — prohibited other driverless taxi services, these services would not have qualified as “on-demand motor vehicle networks” and would not have benefitted from an express authorization. As revised, however, SB 995 now broadens the scope of “on-demand automated motor vehicle networks” to include both those in which a vehicle manufacturer is the only participant (still called a “SAVE Project”) and those in which such a manufacturer merely “supplie[s] or control[s]” the vehicles used therein (not called a “SAVE Project”).

Making sense of all this is difficult. The currently proposed language could mean that automated driving is lawful only in the context of research and development and “on-demand motor vehicle networks.” Or it could mean that automated driving is lawful generally and that these networks are subject to more restrictive requirements. It could mean that any company could run a driverless taxi service, including motor vehicle manufacturers that might otherwise face unrelated and unspecified legal impediments. Or it could mean that a company seeking to run a driverless taxi service must partner with a motor vehicle manufacturer — or that such a company must at least purchase production vehicles, the modification of which might then be restricted by SB 927 and 928 (see below). It could also mean that municipalities could regulate and tax only those driverless taxi services that do not involve a manufacturer. Or that any vehicle manufacturer that wants to run a “SAVE Project” may not bring on any other project partners. Or, because “on-demand automated motor vehicle network” and “participating fleet” are each defined by circular reference to the other, it could mean something else altogether. Clarifying these provisions is a necessary condition to evaluating them.

What’s rough

Like earlier bills in Michigan and other states, SB 995 and 996 understandably struggle to reconcile an existing vehicle code with automated driving. Under existing Michigan law, a “driver” is “every person who drives or is in actual physical control of a vehicle,” an “operator” is “a person, other than a chauffeur, who “[o]perates” either “a motor vehicle” or “an automated motor vehicle,” and “operate” means either “[b]eing in actual physical control of a vehicle” or “[c]ausing an automated motor vehicle to move under its own power in automatic mode,” which “includes engaging the automated technology of that automated motor vehicle for that purpose.” The new bills would not change this language, but they would further complicate these concepts in several ways:

• SB 995 makes reference to “operation without a human operator” and to “[a]utomated technology … that has the capability to assist, make decisions for, or replace a human operator.”
• The bills specify that “[w]hen engaged, an automated driving system” (in SB 995) or “an automated driving system or any remote or expert-controlled assist activity” (in SB 996) “shall be considered the driver or operator of the [or a] vehicle for purposes of determining conformance to any applicable traffic or motor vehicle laws.”
• The bills further provide that this system (plus, in the case of SB 996, “expert-controlled assist activity”) “shall be deemed to satisfy electronically all physical acts required by a driver or operator of the vehicle.”
• SB 995 provides both that “[a] person may operate a platoon … if the person files a plan” with the state and that “the operator of a truck or truck tractor that is in a platoon shall allow reasonable access for other vehicles.”
• The bill further provides that “an on-demand motor vehicle network may be operated.”
• The bill additionally provides that developers of any “technology that allows a motor vehicle to operate without a human operator” shall ensure that the vehicle “is operated only by an employee” or other authorized person and imposes requirements on “[t]he individual operating the vehicle … and the individual who is monitoring the vehicle” — individuals who are, definitionally, the same individual.
• The bill also provides that a current prohibition on using mobile communications devices “while operating a motor vehicle that is moving” does not apply either to “a person using an on-demand automated motor vehicle network” or, in a particularly striking sentence, to “an individual who is using” that device to “operate … an automated motor vehicle while … operating the automated motor vehicle without a human operator.” (Read that a few times.)

This is, collectively, a mess.

If these bills are enacted, drivers and operators could conceivably include companies running driverless taxi services, engineers who start automated vehicles, passengers who merely ride in them (since otherwise a mobile device exception would be unnecessary), companies that file platooning permits, and the automated driving systems themselves. The bills accordingly complicate rather than clarify the meaning of these two critical terms. These terms are critical because Michigan’s current vehicle code places a wide array of rights and responsibilities on the driver or operator of a motor vehicle. Provisions such as the basic seatbelt requirement or the entire licensing regime make no sense when applied to something other than a natural person. And provisions that impose criminal penalties make no sense when applied to something, like an “automated driving system,” that is not even a legal person. This is an important distinction between state vehicle codes, which necessarily treat drivers as legal entities, and the National Highway Traffic Safety Administration’s Federal Motor Vehicle Safety Standards, which do not. (This is why NHTSA’s suggestion that a “self-driving system” could be the “driver” in the limited context of the FMVSS is not as revolutionary as popularly reported.)

Consider the provision that “an automated driving system … shall be considered the driver or operator … for purposes of determining conformance to any applicable traffic or motor vehicle laws.” This provision says nothing about who or what the driver is for purposes of determining liability for a violation of those laws, particularly when there is no crash. SB 996 does provide that “a motor vehicle manufacturer shall assume liability for each incident in which the automated driving system is at fault,” subject to the state’s existing insurance code — but only for SAVE projects. (The additional qualification — “during the time that an automated driving system is in control of a vehicle” — is both unnecessary and insufficiently broad.)

Moreover, the provision that the automated driving system (or possibly the “expert-controlled assist activity”) “shall be deemed to satisfy electronically all physical acts required by a driver or operator of the vehicle” is unclear. The drafters may have intended this to establish that an automated driving system that accomplishes the same ends as a human driver is not unlawful merely because it uses different means. The most natural reading of the actual words, however, is that an automated driving system is deemed to satisfy any and every requirement for physical action, even if it does not achieve an equivalent end.

Applying the existing vehicle code (and other related codes) in the context of automated driving requires much more careful thought. Potential approaches range from wholly revising these codes to accommodate both automated and conventional driving to wholly exempting automated driving and regulating it under a separate regime. Michigan’s bills — as well as inchoate efforts in other states — attempt to take the middle ground by categorically mapping existing law onto automated driving. In 2012, I also attempted this by defining key terms such as driver and by specifying particular canons of interpretation; the result was far more systematic than Michigan’s effort — but still far from perfect. If the state’s legislature wishes to continue with a middling approach, it should provide (or empower an agency to provide) much more clarity on the questions of who is and isn’t a driver and how existing codes actually apply.

What’s alarming

SB 927 provides that a person who “intentionally access[es] or cause[s] access to be made to an electronic system of a motor vehicle to willfully … alter … the motor vehicle” is “guilty of a felony punishable by imprisonment for life or any term of years.” SB 928 accordingly, but inconsistently, amends the code of criminal procedure to specify that “access[ing] electronic systems of motor vehicle to obtain data [!] or control of vehicle” is a class A felony punishable by a statutory maximum of life imprisonment. The primary intent of these bills is, I would hope, to prohibit malicious interference with a vehicle. However, the broad language of SB 927 (“A person shall not intentionally access or cause access to be made to an electronic system of a motor vehicle to intentionally destroy, damage, impair, alter, or gain unauthorized control of the motor vehicle”) goes far beyond any such aim. A literal interpretation would make criminals out of manufacturers that send over-the-air updates to their vehicles, vehicle owners who accept such updates, repair shops that run diagnostics checks while fixing vehicles, owners who install new stereos, automated driving startups that modify production vehicles, researchers who test the safety of vehicle electronics, and many others. These bills are particularly troublesome in light of the assertion by some automakers that they alone “own” the software on vehicles that they have already sold. If these bills move forward, they should be limited to instances in which a person acts in willful or wanton disregard for the safety of others.

What’s left

Other provisions could, at a minimum, benefit from careful review. SB 995 adds to an existing requirement that any developer of relevant technologies submit proof of insurance even before “beginning research,” which seems a bit premature. A provision in the same bill for academic and public research references earlier provisions in a way that makes their application unclear. On at least one reading of SB 995, the bill would not prohibit the wholly unsupervised operation of a lone commercial vehicle but would require a driver behind the wheel if that same vehicle is part of a platoon. Many issues like these might be caught and corrected in the normal legislative process (or not), but the cumulative effect at this point is to create unnecessary confusion about the actual content and effect of a potentially historic set of bills.

For steps that governments can take now to encourage the development, deployment, and use of automated driving systems, please see How Governments Can Promote Automated Driving, available at newlypossible.org.

The long awaited list of recommendations and potential regulations for robocars has just been released by the National Highway Traffic Safety Administration (NHTSA), the federal agency that regulates car safety and safety issues in car manufacture. Normally, NHTSA does not regulate car technology before it is released into the market, and the agency, while it says it is wary of slowing down this safety-increasing technology, has decided to do the unprecedented — and at a whopping 115 pages.

Broadly, this is very much the wrong direction. Nobody — not Google, Uber, Ford, GM or certainly NHTSA — knows the precise form these cars will have when deployed. Almost surely something will change from our existing knowledge today. They know this but still wish to move. Some of the larger players have pushed for regulation. Big companies like certainty. They want to know what the rules will be before they invest. Startups thrive better in the chaos, making up rules as they go along.

NHTSA hopes to define “best practices” but the best anybody can do in 2016 is lay down existing practices and conventional wisdom. The entirely new methods of providing safety that are yet to be invented won’t be in such a definition.

The document is very detailed, so it will generate several blog posts of analysis. Here I present just initial reactions. Those reactions are broadly negative. This document is too detailed by an order of magnitude. Its regulations begin today, but fortunately, they are also accepting public comment. The scope of the document is so large, however, that it seems extremely unlikely that they would scale back this document to the level it should be at. As such, the progress of robocar development in the USA may be seriously negatively affected.

Vehicle performance guidelines

The first part of the regulations is a proposed 15 point safety standard. It must be certified (by the vendor) that the car meets these standards. NHTSA wants the power, according to an Op-Ed by no less than President Obama, to be able to pull cars from the road that don’t meet these safety promises.

Framework for vehicle performance guidance. Credit: NHTSA, Federal Automated Vehicles Policy

• Data Recording and Sharing
• Privacy
• System Safety
• Vehicle Cybersecurity
• Human Machine Interface
• Crashworthiness
• Consumer Education and Training
• Registration and Certification
• Post-Crash Behavior
• Federal, State and Local Laws
• Operational Design Domain
• Object and Event Detection and Response
• Fall Back (Minimal Risk Condition)
• Validation Methods
• Ethical considerations

As you might guess, the most disturbing is the last one. As I have written many times, the issue of ethical “trolley problems” where cars must decide between killing one person are another are a philosophy class tool, not a guide to real-world situations. Developers should spend as close to zero effort on these problems as possible since they are not common enough to warrant special attention. If not for our morbid fascination with machines making life or death decisions in hypothetical situations. Let the policymakers answer these questions if they want to; programmers and vendors don’t.

For the past couple of years, this has been a game that’s kept people entertained and ethicists employed. The idea that government regulations might demand solutions to these problems before these cars can go on the road is appalling. If these regulations are written this way, we will delay saving lots of real lives in the interest of debating which highly hypothetical lives will be saved or harmed in ridiculously rare situations.

NHTSA’s rules demand that ethical decisions be “made consciously and intentionally.” Algorithms must be “transparent” and based on input from regulators, drivers, passengers and road users. While the section makes mention of machine learning techniques, it seems in the same breath to forbid them.

Most of the other rules are more innocuous. Of course, all vendors will know and have little trouble listing what roads their car works on, and they will have extensive testing
data on the car’s perception system and how it handles every sort of failure. However, the requirement to keep the government constantly updated will be burdensome. Some vehicles will be adding streets to their route map literally ever day.

While I have been a professional privacy advocate, and I do care about just how the privacy of car users is protected, I am frankly not that concerned during the pilot project phase about how well this is done. I do want a good regime — and even the ability to do anonymous taxi — so it’s perhaps not too bad to think about these things now, but I suspect these regulations will be fairly meaningless unless written in consultation with independent privacy advocates. The hard reality is that during the test phase, even a privacy advocate has to admit that the cars will need to make very extensive recordings of everything they can, so that any problems encountered can be studied and fixed and placed into the test suite.

50 state laws

NHTSA’s plan has been partially endorsed by the self-driving coalition for safer streets (whose members include big players Ford, Google, Volvo, Uber and Lyft.) They like the fact that it has guidance for states on how to write their regulations, fearing that regulations may differ too much state to state. I have written that having 50 sets of rules may not be that bad an idea because jurisdictional competition can allow legal innovation and having software load new parameters as you drive over a border is not that hard.

In this document, NHTSA asks the states to yield to the DOT on regulating robocar operation and performance. States should stick to registering cars, rules of the road, safety inspections and insurance. States will regulate human drivers as before, but the feds will regulate computer drivers.

States will still regulate testing, in theory, but the test cars must comply with the federal regulations.

New authorities

A large part of the document just lists the legal justifications for NHTSA to regulate in this fashion and is primarily for policy wonks. Section 4, however, lists new authorities NHTSA is going to seek in order to do more regulation.

Some of the authorities they may see include:

• Pre-market safety assurance: Defining testing tools and methods to be used before selling
• Pre-market approval authority: Vendors would need approval from NHTSA before selling, rather than self-certifying compliance with the regulations
• Hybrid approaches of pre-market approval and self-certification
• Cease and desist authority: The ability to demand cars be taken off the road
• Exemption authority: An ability to grant rue exemptions for testing
• Post-sale authority to regulate software changes
• Much more

Other quick notes:

• NHTSA has abandoned their levels in favour of the SAE’s. The SAE’s were almost identical of course, with the addition of a “level 5” which is meaningless because it requires a vehicle that can drive literally everywhere, and there is not really a commercial reason to make a car at present that can do that.
• NHTSA is now pushing the acronym “HAV” (highly automated vehicle) as yet another contender in the large sea of names people use for this technology. (Self-driving car, driverless car, autonomous vehicle, automated vehicle, robocar etc.)

More analysis is on the way.

Source: US Department of Transportation.

With the recent announcement, the US Department of Transportation is enthusiastically embracing automated driving. It’s saying that self-driving vehicles are coming in some form (or many forms) and that the agency can play a role not only in supervising but also in assisting this transportation transformation. The DOT is recognizing the wide range of relevant technologies, applications, and business models and is striving to address them more quickly and flexibly through its wide range of prospective and retrospective regulatory tools. (To be pithy: It’s not your father’s Oldsmobile; it can’t be your father’s DOT.)

https://www.youtube.com/watch?v=YSYjxXfdBcs

As always, the devil will be in the details. Those details will become clearer when the agency releases its full guidance on Tuesday and then as that guidance is (1) revised after public comment (for which the DOT wisely provided), (2) augmented by a related report by the American Association of Motor Vehicle Administrators, and (3) actually applied. For example, the DOT has not yet indicated whether its model state policy will be permissive or restrictive, particularly with respect to actual demonstration projects and even commercial deployments.

I would also expect that this guidance will be the starting point for more thoughtful legislative discussions — not only at the state level but also, for the first time, at the federal level. It will be interesting to see which developers carry the DOT’s implicit requests for new authorities and resources to Congress. The model state policy does not bind states, and some may well decide not to follow it. The performance guidance likewise does not bind developers of automated driving systems, but I would expect few of these developers to deviate from it. This soft guidance could become even more influential if states incorporate it in legislation, if the DOT’s National Highway Traffic Safety Administration (NHTSA) considers it in the course of exemption or enforcement decisions, or if courts look to it to understand how a reasonable developer should act. In other words, DOT is establishing expectations.

You can read the Federal Automated Vehicles Policy – September 2016 here.

Accordingly, I’m especially pleased to see the DOT’s recommendation that these developers undertake — and share — a 15-point safety assessment. Given its limited resources and the speed of technological change, NHTSA cannot hope to come up with all the answers for automated driving in its many forms. But NHTSA can focus on asking the important questions. To that end, this safety assessment encourages developers to share what they are doing, why they think it is safe, and why they should be believed. This is what I have recommended on many occasions (including here, here, and here).

Finally, a note on “driver.” I’ve noted a lot of confusion about NHTSA’s earlier recognition that an automated driving system can be a “driver” for the purpose of the federal motor vehicle safety standards. That limited recognition did not change and could not change how states define drivers for the purpose of their laws or what legal obligations states place on these drivers. Similarly, DOT’s statements today about software do not represent some dramatic change. Because of its authority over motor vehicle design, NHTSA has always had authority over motor vehicle software, which is already ubiquitous on vehicles. (Indeed, in the aftermath of the Toyota’s sudden unintended acceleration debacle, the National Academies released a major report on this very topic.) NHTSA would not change state law by continuing to exercise this authority. For example, NHTSA might recommend (and may one day require) that automated driving software be designed to generally obey speed limits, but states would still set those speed limits.

For DOT’s actual guidance, check out transportation.gov/AV. For more thoughts on what governments can do, check out How Governments Can Promote Automated Driving at newlypossible.org.

If you liked this post, you may also want to read:

• Why Tesla’s purported liability ‘fix’ is technically and legally questionable
• Two New Year’s resolutions for developers of automated vehicles
• 50 different state regulations for robocars is not a bad idea
• Automated vehicle crashes

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Tesla Model S autopilot-software. Source: Tesla

Tesla’s spat with MobilEye reached a new pitch this week with Tesla announcing a new release of their autopilot and plans. As reported earlier, MobilEye announced during the summer that they would not be supplying the new and better versions of their EyeQ system to Tesla. Since that system was/is central to the operation of the Tesla Autopilot, they may have been surprised that MBLY stock took a big hit after the announcement (though it recovered for a while, and is now back down) and TSLA did not.

Statements and documents now show a nastier battle with MobilEye saying they were worried about Tesla using their tool in an unsafe way, invoking all the debate about the fatality and other crashes allegedly caused by people who were lulled into not bothering to supervise the Autopilot. Tesla says that, instead, they have been developing their own advanced vision tools and that MobilEye was afraid and told Tesla if they wanted more EyeQ chips, they would need to halt the competing project and commit to ME. That’s a nasty spat.

Tesla’s own efforts represent a threat to MobilEye from the growing revolution in neural network pattern matchers. Computer vision is going through a big revolution. MobilEye is a big player in that revolution because their ASICs do both standard machine vision functions and can do neural networks. An ASIC will beat a general purpose processor when it comes to cost, speed, and power, but only if the ASIC’s abilities were designed to solve those particular problems. Since it takes years to bring an ASIC to production, you have to aim right. MobilEye aimed pretty well, but at the same time, lots of research out there is trying to aim even better or do things with more general purpose chips, like GPUs. Soon we will see ASICs aimed directly at neural network computations. To solve the problem with neural networks, you need the computing horsepower, and you need well designed deep network architectures, plus, you need the right training data and lots of it. Tesla and ME both are gaining lots of training data. Many companies, including Nvidia, Intel and others are working on the hardware for neural networks. Most people would point to Google as the company with the best skills in architecting the networks, though there are many doing interesting work there. (Google’s DeepMind built the tools that beat humans at the seemingly impossible game of Go, for example.) It’s definitely a competitive race.

Using radar

While Tesla works on their vision systems, they also announced a plan to make much more use of radar. That’s an interesting plan. Radar has been the poor 3rd-class sensor of the robocar, after LIDAR and vision. Everybody uses it — you would be crazy not to unless you need to be very low cost. Radar sees further than the other systems, and it tells you immediately how fast any radar target you see is moving relative to you. It sees through fog and other weather, and it can even see under and around big cars in front of you as it bounces off the road and other objects. It’s really good at licence plates, as well.

What radar doesn’t have is high resolution. Today’s automotive radars have gotten good enough to tell you what lane an object like another car is in, but they are not designed to have any vertical resolution — you will get radar returns from a stalled car ahead of you on the road and a sign above that lane, and not be sure of the difference. You need your car to avoid a stalled car in your lane, but you can’t have a car that hits the brakes every time it sees a road sign or bridge!

mage source: EDN Network

Real world radar is messy. Your antennas send out and receive from a very broad cone with potential signals from other directions and from side lobes. Reflections are coming from vehicles and road users but also from the ground, hills, trees, fences, signs, bushes and bridges. It’s work to get reliable information from it. Early automotive radars found the best solution was to use the doppler speed information, and discard all returns from anything that wasn’t moving towards or away from you — including stalled cars and cross traffic.

One thing that can help (imperfectly) is a map. You can know where the bridges and signs are so you don’t brake for them. Now you can brake for the stalled cars and the cross traffic the Tesla failed to see. You still have an issue with a stalled car under a bridge or sign, but you’re doing a lot better.

There’s a lot of room for improvement in radar and I will presume — Tesla has not said — that Tesla plans to work on this. The automotive radars everybody buys (from companies like Bosch) were made for the ADAS market — adaptive cruise control, emergency braking etc. It is possible to design new radars with more resolution (particularly in the vertical) and other approaches. You can also try for more resolution, particularly by splitting the transmitter and receiver to produce a synthetic larger aperture. You can go into different bands and get more bandwidth and get more resolution in general. You can play more software tricks, and particularly, you can learn by examining not just single radar returns, but rather the pattern of returns over time. (After all, humans don’t navigate from still frames, we depend on our visual system’s deep evolved ability to use motion and other clues to understand the world.)

The neural networks are making strides here. For example, while pedestrians produce basic radar returns, it turns out that their walking stride has a particular pattern of changes that can be identified by neural networks. People are doing research now on how examining the moving and dynamic pattern of radar returns can help you get more resolution and also identify shapes and motion patterns of objects and figure out what they are.

I will also speculate that it might be possible to return to a successor of the “sweeped” radars of old, the ones we are used to seeing in old war movies. Modern car radars don’t scan like that, but I have to wonder if with new techniques, like phased arrays to steer virtual beams (already the norm in military radar) and modern high-speed electronics, that we might produce radars that get a better sense of where their target is. We’re also getting better at sensor fusion — identifying a radar target in an image or LIDAR return to help learn more about it.

The one best way to improve radar resolution would be to use more bandwidth. There have been experiments in using ultrawideband signals in the very high frequencies which may offer promise. As the name suggests, UWB uses a very wide band, and it distributes its energy over that very wide band, which means it doesn’t put too much energy into any one band and has less chance of interfering in those bands. It’s also possible that the FCC, seeing the tremendous public value that reliable robocars offer, might consider opening up more spectrum for use in radar applications using modern techniques and thus increase the resolution.

In other words, Tesla is wise to work on getting more from radar. With the loss of all MobilEye’s vision tools, they will have to work hard to duplicate and surpass that. For now, Tesla is committed to using parts that are for sale for existing production cars, costing hundreds of dollars. That has taken LIDAR “off their radar” even though almost all research teams depend on LIDAR and expect LIDAR to be cheap in a couple of years. (Including the LIDAR from Quanergy, a company I advise.)

Comma announces a $1,000 autopilot box

I wrote earlier about comma.ai and their efforts to drive with just vision, radar and neural networks. They now plan to offer a box for $1,000 to give you some basic autopilot functionality as an add-on.

To do this, they are working with only some specific car models, namely some Honda vehicles that already have advanced ADAS in them. Using the car’s internal bus, they can talk to the sensors in these cars (such as the radar and possibly the camera) and also send control signals to actuate the steering, brakes and throttle. Then their neural networks can take the sensor information, and output the steering and speed commands to keep you in the lane. (Details are scant so I don’t know if the Comma One box uses its own camera or depends on access to the car’s.)

When I rode in Comma’s prototype it certainly wasn’t up to the level of the Tesla autopilot or some others, but it has been several months so I can’t judge it now. Like the Tesla autopilot, the Comma will not be safe enough to drive the car on its own, and you will need to supervise and be ready to intervene at any time. If you get complacent, as some Tesla drivers have, you could get injured or killed. I have yet to learn what measures Comma will take to make sure people keep their eyes on the road.

Generally, I feel that autopilots are not very exciting products when you have to watch them all the time — as you do — and that bolt-on products are not particularly exciting. Cruise’s initial plan (after they abandoned valet parking) was a bolt-on autopilot, but they soon switched to trying to build a real vehicle, and that got them the huge $700M sale to General Motors.

But for Comma, there is a worthwhile angle. Users of this bolt-on box will be helping to provide training data to improve their systems. In fact, they will be paying for the privilege of testing the system and training it. Something that companies like Google did the old-fashioned way, paying a staff of professionals to drive the cars and gather data. For a tiny, young startup it’s a worthwhile approach.

If you liked this article, you may also want to read:

• Robocar recap: Uber buys Otto, folks leave Google, Ford goes big, Tesla drops MobilEye
• Will robocars be heaven or hell for our cities?
• Robocar platooning, or just carpool?
• Understanding the massive gulf between the Tesla Autopilot and a real robocar, in light of the crash

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Two self-driving car events of note: Uber just began operating a fleet of Volvo self-driving cars in Pittsburgh, and nuTonomy launched the first autonomous pilot taxi program last month in Singapore. Both still require a driver, although he/she will be as hands-off as much as possible.

Both Uber and nuTonomy are offering “fully autonomous” or full self-driving automation technology for hire. In 2013, the US Department of Transportation issued a ‘guidance‘ that defined five levels of automation. A ‘Level 4’ autonomous vehicle “is designed to perform all safety-critical driving functions and monitor roadway conditions for an entire trip. Such a design anticipates that the driver will provide destination or navigation input, but is not expected to be available for control at any time during the trip. This includes both occupied and unoccupied vehicles.”

Most cars with functional autonomy right now are at Level 3, with limited self-driving automation: the driver is expected to be available for control when necessary. It’s a big step from Level 3 to Level 4, but the benefits are significant: in addition to leaving the driving completely to the car, it also means that the car is capable of driving itself with no human inside, which is what makes a robotic taxi service possible and why there is so much interest in the success of these services.

nuTonomy

NuTonomy said its new “robo-taxi” service, which includes specially configured Renault Zoe and Mitsubishi i-MiEV electric vehicles, will run within Singapore’s One-North business district, where the company has been conducting daily autonomous vehicle testing since April. The vehicles are fully autonomous but an engineer from the company will ride in the vehicle both to monitor its performance and to take over control if required at some point for safety or other reasons.

The pilot program uses smartphone app-based Uber-like call, instruct and pay software.

The program is the result of an agreement between NuTonomy, a Cambridge, MA-based startup of two former MIT robotics and intelligent vehicle technology engineers, and Singapore’s Land Transport Authority (LTA), for trials of an autonomous mobility-on-demand transportation service.

Uber

The launch of Uber’s self-driving pilot program unveils Uber’s secretive autonomous vehicles program and marks the first time self-driving cars have been so freely available to the public.

Quoting from a Reuters story:

“Uber provided ridealongs to reporters on Tuesday. During a ride of about one hour, Reuters observed the Uber car safely – and for the most part smoothly – stop at red lights and accelerate at green lights, travel over a bridge, move around a mail truck and slow for a driver opening a car door on a busy street. All without a person touching the controls.”

Uber’s Pittsburgh fleet consists of Volvo SUVs and Ford Fusion cars outfitted with 3D cameras, global positioning systems (GPS) and a LiDAR that uses lasers to assess the shape and distance of objects, all mounted with seeming lack of design to the vehicle’s roof.

Pittsburgh is full of steep hills and narrow streets, potholes, tunnels and more than 440 bridges. It has snow and ice in the winter, blossoming trees that can hide street signs and traffic signals in the spring, blinding sun in the summer and a slippery ground cover of fallen leaves in the autumn. But Pittsburgh also offers Uber city leadership who have rolled out the red carpet plus helped pass a state law that allows for autonomous cars (as long as someone is behind the wheel to take over if needed).

Thus the merits of Uber’s new program: if Uber can do it in Pittsburgh, they can do it anywhere.

Are self-driving cars robots? What is a robot?

The International Federation of Robotics (IFR) and the Geneva-based International Organization for Standardization (ISO) have had an established definition for industrial robots for many years. Service robots, the everything else category, represents all other non-industrial robots. That’s a tough category to define because it’s a moving target as the industry grows and technologies improve. Now with questions rising about whether Level 3, 4 and 5 cars are robotic, or whether many consumer drones are just remote-controlled, the IFR has decided to hold workshops in October and November and release their findings and suggestions for redefining what is a robot by the end of the year.

Here is the present IFR service robot definition:

• A robot is an actuated mechanism programmable in two or more axes with a degree of autonomy, moving within its environment, to perform intended tasks. Autonomy in this context means the ability to perform intended tasks based on current state and sensing, without human intervention.
• A service robot is a robot that performs useful tasks for humans or equipment excluding industrial automation application.
• A personal service robot or a service robot for personal use is a service robot used for a non-commercial task, usually by lay persons. Examples are domestic servant robot, automated wheelchair, personal mobility assist robot, and pet exercising robot.
• A professional service robot or a service robot for professional use is a service robot used for a commercial task, usually operated by a properly trained operator. Examples are cleaning robot for public places, delivery robot in offices or hospitals, fire-fighting robot, rehabilitation robot and surgery robot in hospitals. In this context, an operator is a person designated to start, monitor and stop the intended operation of a robot or a robot system.

[A robot system is a system comprising robot(s), end-effector(s) and any machinery, equipment, devices, or sensors supporting the robot performing its task.]

An industrial robot, as defined by ISO 8373:

• An automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes, which may be either fixed in place or mobile for use in industrial automation applications;
• Its programmed motions or auxiliary functions may be changed without physical alterations;
• It is capable of being adapted to a different application with physical alterations of the mechanical structure or control system except for changes of programming cassettes, ROMs, etc.

If you liked this article, you may also be interested in:

• Robohub roundtable: Why is it so difficult to define ‘robot’?
• What to expect from autonomous cars
• What do people think about autonomous cars? Results from our reader poll
• Volvo says it will accept “full liability” when its cars are operating autonomously
• Breaking down robotaxi economics
• Robots Podcast #212: Self-driving Cars: From Research to Road, with Karl Iagnemma

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Source: US DOT

At the recent AUVSI/TRB conference in San Francisco, there was talk of upcoming regulation, particularly from NHTSA. Secretary of Transportation Foxx and his NHTSA staff spoke with just vague hints about what might come in the proposals due this fall. Generally, they said good things, namely that they are wary of slowing down the development of the technology. But they said things that suggest other directions.

Secretary Foxx began by agreeing that the past history of automotive driving systems was quite different. Regulations have typically been written years or decades after technologies have been deployed. And written regulations tend to involve standards which the vendors self-certify their compliance with. What this means is that there is not a government test center that confirms a car complies with the rules in the safety standards. Instead, the vendor certifies they are following the rules. If they certify falsely, that can get them in trouble later with regulators, and more importantly, in lawsuits. It’s by far the best approach unless the vendors have shown that they can’t be trusted in spite of the fear of these actions.

But Foxx said that they were going to go against that history and consider “pre-market regulation.” Regular readers will know I think that’s an unwise idea, and so do many regulators, who admit that we don’t know enough about the final form of the technology to regulate yet.

Fortunately, it was also suggested that NHTSA’s new documents would be more in the form of “guidance” for states. Many states ask NHTSA to help them write self-driving car regulations. Which gets us to a statement that was echoed by several speakers to justify federal regulation, “Nobody wants 50 different regulations” on these cars.

At first, that seems obvious. I mean, who would want it to be that complex? Clearly, it’s simpler to have to deal with only one set of regulations. But while that’s true, it doesn’t mean it’s the best idea. They are overestimating the work involved in dealing with different regulations, and underestimating the value of having the ability for states to experiment with new ideas in regulation, and the value of having states compete on who can write the best regulations.

If regulations differed so much between states as to require different hardware, that makes a stronger case. But most probable, we are talking about rules that affect the software. That can be annoying, but it’s just annoying. A car can switch what rules it follows in software when it crosses a border with no trouble. It already has to, just because of the different rules of the road found in every state, and indeed, every city and even every street! Having a few different policies state-by-state is no big addition.

Jurisdictional competition is a good thing though, particularly with emerging technologies. Let some states do it wrong, and others do it better, at least at the start. Let them compete to bring the technology first to their region, and invent new ideas on how to regulate something the world has never seen. Over time these regulations can be normalized. By the time people are making 10s of millions of robocars, that normalization will make more sense. But most vendors only plan to deploy in just a few states to begin with anyway. If a state feels its regulations are making it harder for the cars to spread to its cities, it can copy the rules of the other state it likes best.

The competition assures any mistake is localized — and probably eventually fixed. If California follows through with banning unmanned operation, as they have proposed, Texas has said it won’t.

I noted that if the hardware has to change, that’s more of an issue. It’s still not that much of an issue, because cars that operate as taxi services will probably never leave their base state. Most of them will have limited operational zones, and except in cities that straddle state borders, they won’t even leave town, let alone leave the state. Some day, the cars might do interstate trips, but even then you can solve this by having one car drive you to the border and then transfer to a car for the other state. Annoying, but only slightly, and not a deal-breaker on the service. A car you own and take on road trips is a different story.

The one way having different state regulations would be a burden would be if there were 50 different complex certification processes to go through. Today, the federal government regulates how cars are made and the safety standards for that. The states regulate how cars operate on the roads. Robocars do blur that line, because how they are made controls how they drive.

For now, I still believe the tort system — even though it differs in all 50 states — is the best approach to regulation. It already has all developers highly paranoid about safety. When the day comes for certification, a unified process could make sense, but that day is still very far away. But for the regulations of just how these cars will operate, it might make sense to keep that with the states, even though it’s now part of the design of the car rather than the intentions of a human driver.

In time, unified regulations will indeed be desired by all, once we’ve had the time to figure out what the right regulations should be. But today? It’s too soon. Innovation requires variety.

Trucks platooning on the motorway. Photo: Dan Boman

At the recent AUVSI/TRB symposium, a popular research topic was platooning for robocars and trucks. Platooning is perhaps the oldest practical proposal when it comes to car automation because you can have the lead vehicle driven by a human, even a specially trained one, and thus, resolve all the problems that come from road situations too complex for software to easily handle.

Some early experiments indicate fuel savings, though relatively modest ones. At practical distances, you can see about 10% saving for following vehicles and 5% for the lead vehicle. Unfortunately, a few big negatives showed up. It’s hard to arrange platoons, errors can become catastrophic multi-car pile-ups, other drivers keep inserting themselves into the gap unless it’s dangerously small, and the surprising deal-breaker that comes from the stone chips which are thrown up by lead vehicles which destroy the finish — and in some cases the radiator or windshield — of following cars. They can also create a congestion problem and highway exit problem the way existing convoys of trucks sometimes do that.

One local company named Peloton is making progress with a very simple platooning problem. They platoon two (and only two) trucks on rural highways. The trucks find one another over the regular data networks, and when they get close they establish a local radio connection (using the DSRC protocol that many mistakenly hope will be the standard for vehicle to vehicle communications.) Both drivers keep driving, but the rear driver goes feet-off-the-pedals like a cruise control. The system keeps the vehicles a fixed distance to save fuel. The trucks don’t mind the stone chips too much. Some day, the rear driver might be allowed to go in the back and sleep, which would allow cargo to move 22 hours/day at a lower cost, probably similar to the cost of today’s team driving (about 50 cents/mile) but with two loads instead of one.

Trucks are an easy win, but I also saw a lot of proposals for car platoons. Car platoons are meant to save fuel, but also to increase road capacity. But after looking at all the research, a stronger realization came to me. If you have robocars, why would you platoon when you can carpool?. To carpool, you need to find two cars who are going to share a long segment of the trip together. Once you have found that, however, you get far more savings in fuel and road usage if the cars can quickly pause together and the passengers from one transfer into the other. Then the empty car can go and move other commuters. This presumes, of course, that the cars are like almost all cars out there today, with many empty seats. When the groups of passengers come to where their path diverts, the vehicle would need to stop at a transfer point, and some passengers would move into waiting robotaxis to take them the rest of the way.

Source: peloton-tech

All of this is not as convenient as platooning, which in theory can happen without slowing down and finding a transfer point. This is one reason that the carpool transfer stations I wrote about last month could be a very useful thing. Such stations would add only 1-2 minutes of delay, and that’s well worth it if you consider that compared to platooning, this carpooling means a vastly greater fuel saving (almost 50%) and a much greater increase in road capacity, with none of the other downsides of platooning.

If you’re thinking ahead, however, you will connect this idea to my proposed plan for the future of group transit. The real win is to have the computers of the transport service providers notice the common routes of passengers early, before they even get into a vehicle, and thus pool them together with minimal need to stop and switch cars.

Some folks have imagined designing cars that can physically couple, which would produce very efficient platoons and not add a delay. The problem (aside from the difficulties in doing this safely) is that this requires a physical standard, and physical standards are much harder to get working than software ones. It requires you find a platooning partner who has the same hardware you do, rather than software platooning, which can work with any style of car. Automated matching and carpooling makes no requirements on the individual robocars and their design, which gives it the best path to success.

It is possible (though a bit frightening) to imagine a special bus which could dock to robocars to allow transfer of passengers at speed. Some of you may have seen that a Chinese company has built the formerly hypothetical straddling bus (actually a train) that has cars drive under it. If you were assured a perfectly smooth road one could imagine a docking extension which could surround a car door of a perfectly synced robocar and allow a transfer. I suspect that’s all pretty far in the future.

Beyond the carpool

In a robocar world, we should see a move to having vehicles with fewer empty seats. This happens if more people use single person vehicles for their solo trips, and as carpooling and other technologies make sure that the four-seater vehicles end up with more people. Indeed, if the carpooling works, that happens naturally. At that point one might say, “now’s the time to platoon.” There is merit to that, but it comes later, rather than sooner. At this later date, we can be more comfortable with the safety, and have a greater density of vehicles making it more likely to find others vehicles ready to platoon. Of course, we’ll also have more vans and buses on the road who can combine even larger groups, if you find groups with a lot of journey in common. Platooning is practical even for a few miles, while carpooling tends to need a longer amount of shared journey to make it worth the switch.

At that point in the technology, you can do much more serious platoons, with larger groups of cars, and distances which are short enough for even greater benefit, and quick enough to strongly discourage people trying to insert themselves in the middle of the platoon.

So platoons will come and give us even more road capacity. Carpooling, though, is already happening, with 50% of Uber requests in San Francisco being done in UberPool mode. It is the more likely early answer.

Never say never? Source: 2011 Dodge Charger, future of driving commercial/YouTube

If you had asked me recently what big car company was the furthest behind when it came to robocars, one likely answer would be Fiat-Chrysler. In fact, famously, Chrysler ran ads several years ago during the superbowl making fun of self-driving cars and Google in particular:

Now Google has announced a minor partnership with Chrysler where they will be getting Chrysler to build 100 custom versions of their hybrid minivans for Google’s experiments. Minivans are a good choice for taxis, with spacious seating and electric sliding doors, if you want a vehicle to pick you up, it probably should have something like this.

This is a minor partnership, closer to a purchase order than a partnership, but it will be touted as a great deal more. My own feeling is it’s unlikely a major automaker will truly partner with a big non-auto player like Google, Uber, Baidu or Apple. Everybody is concerned about who will own the customer and the brand, and who will be the “Foxconn” and the big tech companies have no great reason to yield on that (because they are big) and the big car companies are unlikely to yield, either. Instead, they will acquire or do deals they control with smaller companies (like the purchase of Cruise or the partnership with Lyft from GM.)

Still, what may change this is an automaker (like FCA) getting desperate. GM got desperate and spent billions. FCA may do the same. Other companies with little underway (like Honda, Peugeot, Mazda, Subaru, Suzuki) may also panic or hope that the Tier 1 suppliers (Bosch, Delphi, Conti) will save them.

Google custom designed a car for their 3rd generation prototype, with 2 seats, no controls and and electric NEV power train. This has taught them a lot, but I bet it has also taught them that designing a car from scratch is an expensive proposition before you are ready to make thousands of them.

The question was asked: “how would a robocar deal with this?” It turns out there are many answers to this question. For this particular question, as you’ll see by the end, the answer is probably “very well.”

Let’s start with the bad news. On its own, prior to this happening, there is a good chance the vehicle would not handle it well. The instinct for most developers is to be conservative and cautious when facing an unknown situation. The most cautious thing is to do nothing, to just stop and perhaps ask for help from a person in the car or a remote center. Usually if you don’t understand the situation, doing something is much riskier than doing nothing. Usually — but clearly not here.

This situation might be viewed as similar to something you might expect a car to have programming for — something is approaching fast towards you. Cars will probably have logic to deal with a car coming the wrong way down their lane, and this looks a bit like that. It’s actually stuff coming in both lanes. We can imagine the car might have logic to attempt to retreat in that situation, though this isn’t going to look too much like anything the sensors have seen before. With 3D sensors, though, it will be clear that something huge is coming fast. And with a map of what the road should look like, you will easily tell the wall of water and debris from what you should be seeing.

The best reason the car might handle this however, is the very existence of this video, and the posts about it — including this very blog post here. The reason is that developers of robocars, in order to test them, are building simulators. In these simulators they are programming every crazy situation they can think of, even impossible situations, just to see what revisions of the car software will do. They are programming every situation that their cars have encountered on the road. Every situation that caused their software to make an error, or anybody else’s to make an error.

In other words, if you can think of it, they probably thought of it too. And if it’s in blog posts and famous news stories, they’ve probably heard about it. Flooding and every kind of strange weather ever reported. The details of every accident from every police report that can be turned into a simulation. Flooding. Earthquakes. Tornadoes. Hurricanes. Alien invasions. Oncoming tanks. If you can think of it without a major effort, and it seems like it could happen, they will put it in. And so, every car will indeed be tested.

In this particular case there is another thing that can help with this situation. Tsunamis don’t happen by surprise. The world, having seen them like this, has earthquake detection and tsunami warning everywhere, in all places robocars are likely to go in the near future. The warnings will be transmitted along the same data stream warning cars about traffic, weather and road conditions. We even have maps of the terrain and can predict what areas are low and which areas cars should head to in the event of a tsunami warning, and they will take routes designed to avoid risk. With superhuman knowledge, they will do much better than people at this, and so they odds of them confronting the wall of water would be slim, unless there was no choice. The car simply would not have been going down that road.

Now we get to a final special ability of robocars — they will be just as capable in reverse gear as they are going forward, other than due to the speed limitations of reverse gear. So while you reverse timidly, a robocar need not do so. It will be able to pull off the fastest 3 point turn you can imagine if it wants to, or even just escape in reverse. Of course if it needs more speed than reverse offers, it would turn around in the best spot to do so. Stanford has even done a lot of research on drifting, and this will go into simulators too, so cars will probably know how to turn around as fast as a stunt driver if they have to. Electric cars may be able to go as fast in reverse as they can going forward to top it all off.

So for this situation, and anything else we know about, robocars should do a superhuman job. That doesn’t mean there aren’t things nobody ever thought of. But, the more videos and stories like this that are recorded, the less and less probable unknown events will be, and thus an unknown event where the software does the wrong thing becomes not impossible, but very low probability.

Platooning. Photo: Dan Boman/Scania

A dozen trucks arrived in Rotterdam after traveling across Europe in the European Truck Platooning Challenge. Six different truck makers participated: Scania, Daimler, Volvo, IVECO, MAN and DAF.

Truck platooning involves a convoy of connected trucks that drive and react as a single unit. Acceleration and braking are linked and controlled by computers and WiFi, allowing vehicles to drive much closer together without sacrificing safety while reducing drag and also the cost of drivers and fuel. Each of the six truck manufacturers demonstrated their own platooning technologies.

It is estimated that fuel savings of up to 10% can be achieved with equivalent reductions in CO2 emissions by this type of slipstreaming.

Each of the test vehicles was connected by WiFi, which allowed for the close platooning that wouldn’t be possible with human drivers. Trucks used an advanced Wifi-P connection, developed by NXP, and specially designed for automotive applications. The hi-speed WiFi system enables trucks to drive close together and communicate with each other. Everything that the driver in the first truck sees in front of him is projected onto a screen in the second truck.

“The technology is still very much an assistance feature,” said Jeremy Carlson, senior analyst for autonomous driving at IHS Automotive. “This type of automated vehicle handles 98 percent of the miles, but you still need to have a human driver ready to take some of the scenarios that come with large trucks, including navigating narrow city roads.”

Truck platooning could happen within the next 10 years and most manufacturers are investing in the technology. Daimler recently announced it was investing more than $500 million into its truck platooning technology, and research reports have forecast that more than 7.7 million truck platooning systems could be on roads around the world by 2025.

Picture the scene. Emergency services are called to attend the accident. A teenage girl on a bicycle apparently riding along a cycle path was hit and killed by a car. The traffic police quickly establish that the car at the centre of the accident was operating autonomously at the moment of the fatal crash. They endeavour to find out what went wrong, but how? Almost certainly the car will have logged data on its behaviour leading up to the moment of the crash – data that is sure to hold vital clues about what caused the accident, but will that data be accessible to the investigating traffic police? And even if it is, will the investigators be able to interpret the data?

There are two ways the story could unfold from here.

Scenario 1: unable to investigate the accident themselves, traffic police decide to contact the manufacturer and ask for help. As it happens, a team from the manufacturer actually arrives on scene very quickly – it later transpires that the car had ‘phoned home’ automatically so the manufacturer actually knew of the accident within seconds of it taking place. Somewhat nonplussed, the traffic police have little choice but to grant them full access to the scene of the accident. The manufacturer undertakes their own investigation and – several weeks later – issue a press statement explaining that the AI driving the car was unable to cope with an “unexpected situation” which “regrettably” led to the fatal crash. The company explain that the AI has been upgraded so that it cannot happen again. They also accept liability for the accident and offer compensation to the child’s family. Despite repeated requests the company declines to share the technical details of what happened with the authorities, claiming that such disclosure would compromise its intellectual property.

A public already fearful of the new technology reacts very badly. Online petitions call for a ban on driverless cars and politicians enact knee-jerk legislation which, although falling short of an outright ban, sets the industry back years.

Scenario 2: the traffic police call the newly established driverless car accident investigation branch (DCAB), who send a team consisting of independent experts on driverless car technology, including its AI. The manufacturer’s team also arrive, but – under a protocol agreed with the industry – their role is to support DCAB and provide “full assistance, including unlimited access to technical data”. In fact, the data logs stored by the car are in a new industry standard format thus access by DCAB is straightforward; software tools allow them to quickly interpret those data logs. Well aware of public concerns DCAB provide hourly updates on the progress of their investigation via social media and, within just a few days, call a press conference to explain their findings. They outline the fault with the AI and explain that they will require the manufacturer to recall all affected vehicles and update the AI, after submitting technical details of the update to DCAB for approval. DCAB will also issue an update to all driverless car manufacturers asking them to check for the same fault in their own systems, also reporting their findings back to DCAB.

A public fearful of the new technology is reassured by the transparent and robust response of the accident investigation team. Although those fears surface in the press and social media, the umbrella Driverless Car Authority (DCA) are quick to respond with expert commentators and data to show that driverless cars are already safer than manually driven cars.

There are strong parallels between driverless cars and commercial aviation. One of the reasons we trust airliners is that we know they are part of a highly regulated industry with an amazing safety record. The reason commercial aircraft are so safe is largely down to the very tough safety certification processes and, when things do go wrong, the rapid and robust processes of air accident investigation. There are emerging standards for driverless cars: ISO Technical Committee TC 204 on Intelligent Transport Systems already lists 213 standards. There isn’t yet a standard for fully autonomous driverless car operation, but see for instance ISO 11270:2014 on Lane keeping assistance systems (LKAS). But standards need teeth, which is why we need standards-based certification processes for driverless cars managed by regulatory authorities – a driverless car equivalent of the FAA. In short, a governance framework for driverless cars.

Postscript: several people have emailed or tweeted me to complain that I seem to be anti driverless cars – nothing could be further from the truth. I am a strong advocate of driverless cars for many reasons, first and most importantly because they will save lives, second because they should lead to a reduction in the number of vehicles on the road – thus making our cities greener, and third because they might just cure humans of our unhealthy obsession with personal car ownership. My big worry is that none of these benefits will flow if driverless cars are not trusted. But trust in technology doesn’t happen by magic and, in the early days, serious setbacks and a public backlash could set the nascent driverless car industry back years (think of GM foods in the EU). One way to counter such a backlash and build trust is to put in place robust and transparent governance as I have tried (not very well it seems) to argue in this post.

Toyota i-Road. Source: Wikipedia Commons

I frequently read that people claim one effect of robocars will be car share (when they work as taxis) and fewer cars being made (because most cars stay idle 95% of the time) — which is good news for everybody, but the car industry. I did some analysis of why that’s not necessarily true and recent analysis shows the problem to be even more complex when I first laid out.

To summarize: in a world of robotic taxis, just like today’s taxis, they don’t wear out by the year any more, they wear out by the mile (or km). For example, taxis in New York last about 5 years and 250,000 miles. Once cars wear out by the mile, the number of cars you need to build is equal to:

Total Vehicle Miles
——————————
Car Lifetime in Miles

As you can see, the simple equation does not involve how many people share the vehicle at all! As long as the car is used enough so the car is not junked before it wears out from miles, nothing changes. It’s never that simple, however, and new factors come into play. The actual model is complex with a lot of parameters — we don’t know enough to make a good prediction.

People travel more in cars

It’s likely that the number of miles people want to travel goes up for a variety of reasons. Robocars make car travel pleasant and convenient. Some might decide to live further from their place of employment since they can work, read, socialize or even sleep on the commute. They might make all sorts of trips more often. Outside of rush hour, they might also be more likely to switch from other modes, such as public transit, and even flying. Consider two places about a 5 hour drive apart — today flying is going to take just under 3 hours due to all the hassles we’ve added to flying, even with the improvements robocars make to those hassles. Many might prefer an uninterrupted car ride where they can work, watch videos or sleep.

Vehicles run empty to reposition

Regular taxis have wasted miles between rides. Indeed, a New York taxi has no passenger 38% of the time. Fortunately, robocars will be a lot more efficient than that, since they don’t need to cruise around looking for rides. Research suggests a more modest 10% “empty mile” cost, but this will vary from situation to situation. If you need the robotaxi fleet to constantly run empty in the reverse commute direction, it could get worse. Among those who believe robocars will be more personally owned than used as taxis, we often see a story painted of how a household has a car that takes one person to work, and returns home empty to take the second person, and then return again to take others on daytime errands. This is possible, but pretty inefficient. I think it’s far more likely that in the long term families will just use other taxi services rather than have their car return home to serve another family member.

Cars last longer

The bottom part of the equation is likely to increase, which reduces the number of cars made. Today, cars are engineered for their expected life-cycle — 19 years and 190,000 miles in California, for example. Once you know your car is going to have a high-duty cycle, you change how you engineer it. In particular, you combine engineering of parts for your new desired life cycle with specific replacement schedules for things that will wear out sooner. You want to avoid junking a car with lots of life in the engine just because the seats are worn out, so you make it easy to replace seats, and have the car bring itself to a service center where that’s fast and easy.

Yes, I am serious about the seats. In a robotaxi world, people will not want to order up a taxi that has a 150,000 mile worn out interior, though they might do it for a discount. Cars of the future will be highly instrumented with sensors. They will know precisely the wear and tear on all components and replace and maintain them in a highly efficient way.

Electric cars, in general, are expected to have certain components last longer because they are simpler and reliable with fewer parts. Electric motors that travel millions of miles are not at all out of the question, if they are the right choice. Battery systems today will wear out faster than that, but that may not be the case in the future. Bodies and interiors will still wear out as they do today, though they will be cleaned and maintained better.

If you look at the no-dashboard layout of the Google prototype, you can also see a path to that being simpler and lasting longer. Fewer parts means easier maintenance and longer life.

In general, you can get a longer lifetime by paying more, and that usually decreases your depreciation cost per mile — ie. paying double more than doubles the miles, though the time value of money must be accounted for. On the other hand, older is usually less valued, even when fully functional and even when the electronics are replaced. The “sweet spot” of cost and lifetime is yet to be revealed.

Electronics go obsolete faster?

Some parts of the car need a shorter lifespan. The computers, sensors and electronics will be improving on the Moore’s law curve. Nobody wants a 5 year old cell phone, and so it’s a good thing that robotaxis will wear out faster — or at least may be designed so their electronic parts can be cheaply swapped out every few years to stay competitive.

Cars get cheaper, way cheaper

The one person robotaxi is a very practical vehicle and it’s possible to make it much cheaper than today’s cars, even with a fairly luxurious interior. The robocar sensor package is already dropping quickly in price, and will soon be under $1,000. Like all computerized electronics, it will keep dropping. One person short range vehicles can be made cheaply in volume. The robotaxi does not need or want a complex dashboard and fancy entertainment system. Your tablet or phone does that for you. Think more about a nice luxurious chair in a box. You’re more likely to want a little fridge, or a desk than what you put in a car today. Urban cars could drop to just a few thousand dollars with mass production. Customers who care about price will demand these cheap rides. Others will demand luxury rides but they will cost less than today’s $80,000 luxury boxes.

Once the price of batteries drops — and that’s going to happen — the electric power trains will also be cheaper than the ICE ones.

Today, an UberX ride costs $2/mile. Owning your own car costs 50-60 cents/mile when new, dropping when the car gets older plus parking. Robocars won’t pay much for parking, and there’s no reason that a robotaxi ride should not cost something similar. And a ride in a one-person $7,000 robotaxi that lasts 500,000 miles? That’s going to cost under 25 cents/mile.

The bad news here for car companies: Even if the equation says they sell more cars, they sell a lot of them for much less money, dropping the dollar volume of their business. (Maybe — see below.)

People share rides, or maybe not

One way total vehicle miles in the equation could go down while personal miles go up is if people share rides more. This requires multi-seat robotaxis, but the success of UberPool and LyftLines, which do shared rides, suggest big potential. Indeed, I see something much grander possible at rush hour that I will describe in my forthcoming article on the future of transit.

Robotaxis for sharing could be much better than what we have now. Even in the traditional car form factor you could see a car with a divider so each passenger has their own private space, not even needing to see the other rider. With good ride coordination people never go far out of their way, and with multiple robotaxis, people never go any distance out of their way ever, but do have to change taxis once or twice in 30 second switch-offs. The potential for very pleasant ride-share service is there, and that could bump vehicle occupancy a lot and thus reduce the top of the equation, and the number of cars made.

But there’s a snag, and it’s the price point described above. Today, with UberX at $2/mile, sharing is an attractive financial proposition. Cut your cost by 30-40% for some inconvenience is a winning offer when the cost of the ride is $10 or especially $50, like my recent airport ride.

But if the cost is 30 cents/mile and your trip is 5 miles, are you going to accept any hassle to reduce the $1.50 cost to $1 or even $0.50? It’s a lot less likely, though it could be likely at peak-use times when there simply aren’t enough vehicles to serve the demand. Indeed, one of the advantages of making larger cars (like today’s 4 passenger vehicles) is that such a fleet has much more ability to absorb peak load by encouraging and eventually requiring pleasant efficient sharing. While it’s more expensive to build and run a 4 seat car than a 1 seat car, it isn’t 4 times as expensive, so the likely fleet does not have the perfect mix of vehicle sizes you might otherwise choose.

Delivery robots reduce shopping trips

You may recall that I am working at Starship Technologies on delivery robots. These delivery robots will be super efficient in energy and road use. Some estimates suggest that 30% of trips today are shopping trips, and we could put a real dent in that, along with the general move to online shopping and delivery. After all, a UPS truck full of Amazon packages is, in a way, people sharing the ride.

But far more people now can afford private car transportation

Of the factors above, sharing rides and making longer-lived cars, this could reduce the number of cars needed and the reduction in car cost reduces the total the world spends on cars (as well as the energy required to build them.) Perhaps those factors might counter the additional travel and the empty miles.

One factor will overwhelm all of this. Cheap robotaxi service under 50 cents/mile will suddenly make personal car transportation economically accessible. Drop to 30 cents/mile or even 10 cents/mile in poorer economies and we’re talking vastly more accessible to billions of new people. The market may already be mostly saturated in the United States, which has vast car ownership, but the global average is about 15%. It’s going to grow, a lot. The car industry is facing a boom not a bust from this technology.

This may sound like a nightmare to those who blame private cars for many of our environmental and urban woes. Fortunately, the picture is not quite the same with these cars which are far more likely to be efficient, low-emitting and sustainable, indeed, more sustainable than the transit systems we use today. (Indeed, they could be combined with a new vision of even more sustainable transit during peak times.)

The parking story is good, though

While I have said this in other places it is worth noting that while car sharing does not reduce the number of cars on the road, it vastly reduces the amount of parking needed. In fact in many places reduces it to zero, that’s good news for everybody.

One reason car sharing companies like Zipcar have published statistics such as “Each zipcar replaces 12 cars” — leading people to the false conclusion that robocars shrink the size of the car industry — is that Zipcar sucks. What I mean is not that it’s a bad product, but compared to robotaxi service (or even services like Car2Go, DriveNow or Uber) it’s terrible. Trips in a typical carshare car discourage car use, because they make it inferior in two ways. First, they have the hassle of going to get the car, and returning it to the same spot, and they make you think about your car travel by the hour so that you understand its real cost. This means you are very unlikely to sign out a zipcar to do a short shopping errand. It’s good for people to understand the cost of what they do, and probably good for cities, but these principles just won’t apply to the low cost robotaxi.

This crash ties into an upcoming article I will be writing about driving in places where everyone violates the rules. I recently landed from a trip to India, which has a chaotic road system (far more chaotic than California) and it got me thinking a bit about problems.

Traffic in Gwalior, India. Source: Wikimedia Commons

Google is thinking about them, too. Google reports it just recently started experimenting with new behaviours; in this case, making a right turn on a red light off a major street where the right lane is extra wide. In that situation it has become common behaviour for cars to effectively create two lanes out of one, with a straight through group on the left, and right turners hugging the curb. The vehicle code would have there be only one lane, and the first person not turning would block everybody turning right, who would find it quite annoying. In India, the lane markers are barely suggestions, and drivers — which consist of every width of vehicle you can imagine dynamically form their own patterns.

As such, Google wanted their car to be a good citizen and hug the right curb when conducting a right turn. So they did, but found the way blocked by sandbags on a storm drain. So they had to “merge” back with the traffic in the left side of the lane. They did this when a bus was coming up on the left and they made the assumption, as many would make, that the bus would yield and slow a bit to let them in. The bus did not, and the Google car hit it at very low speed. The Google car could have probably solved this with faster reflexes and a better read of the bus’ intent, and probably will in time, but more interesting is the question of what you expect of other drivers. The law doesn’t imagine this split lane or this “merge.” And of course the law doesn’t require people to slow down to let you in.

While some people like to imagine that important ethical questions for robocars revolve around choosing who to kill in an accident, that’s actually an extremely rare event. The real ethical issues revolve around this issue of how to drive when driving involves routinely breaking the law — not once in a 100 lifetimes, but once every minute. Or once every second, as is the case in India. To solve this problem, we must come up with a resolution and we must eventually get the law to accept it the same what it accepts it for all the humans out there, who are almost never ticketed for these infractions.

So, why is this a good thing? Google is starting to work on these issues, and you need to solve these problems to drive even in orderly places like California. And yes, you are going to have some mistakes, and some dings on the way there and that’s a good thing, not a bad thing. Mistakes in negotiating who yields to who are very unlikely to involve injury, as long as you don’t involve things smaller than cars (such as pedestrians.) Robocars will need to not always yield in a game of chicken or they cannot survive on the roads.

In this case, Google says it learned that big vehicles are much less likely to yield. In addition, it sounds like the vehicle’s confusion over the sandbags probably made the bus driver decide the vehicle was stuck. It’s still unclear to me why the car wasn’t able to abort its merge when it saw the bus was not going to yield, since the description has the car sideswiping the bus, not the other way around.

Nobody wants accidents — and some will play this accident as more than it is — but neither do we want so much caution that we never learn these lessons.

It’s also a good reminder that even Google, though it is the clear leader in the space, still has lots of work to do. A lot of people I talk to imagine that the tech problems have all been solved and all that’s left is getting legal and public acceptance. There is great progress being made, but nobody should expect these cars to be perfect today. That’s why they run with safety drivers, even before the law demanded it. This time the safety driver also decided the bus would yield and so let the car try its merge. Expect more of this as time goes forward.

This post originally appeared on robocars.com.

Photo credit: John Eckman via Flickr. https://www.flickr.com/photos/jeckman/6183019560

I’ve been electric car shopping, but one thing has stood out as a big concern. Many electric cars are depreciating fast, and it may get even faster. I think part of this is due to the fact that electric cars are a bit more like electronics devices than they are cars. Electric cars will see major innovation in the next few years, as well as a decline in their price/performance of their batteries. This spells doom for their value. It’s akin to cell phones — your 2 year old cell phone still functions perfectly, but you dispose of it for a new one because of the pace of innovation. Electric cars are not at that pace, but they are skirting the phenomenon.

When it comes to robocars, I remind people that the computer will be the most important part of the car — not the engine or other features. And the computer and software are on the Moore’s Law curve, like your phone. The battery system is not like this, but digital features are becoming more and more important parts of every car.

The most obvious cause of the big depreciation is not related to the cars. In the US, there is a $7500 federal tax rebate on a new electric car, so the moment you drive it off the lot, its blue book value drops an additional $7500. In addition, different states offer credits from of up to $5,000, and unless you take the car out of state, that amount will also drop off the value. This is the primary culprit for the huge depreciation numbers, but there is more.

Perversely, people with higher incomes don’t get California’s $2,500 credit, so for them, buying used is a very wise idea, because somebody else got the credit, and it’s reflected in the price of the car. Of course, if you are rich enough, you may tolerate paying $2,500 more than everybody else for the new car. In fact, if not for the sales tax, it would be a good strategy to get somebody else to buy a car for you and get the credit, then buy it from them. Or take over a lease (getting to that…)

There are rumours that vendors might even be trying to subsidize against this depreciation to avoid a collapse in the price of their cars. After all, such low used car value discourages confidence in the car (and steals away buyers of new cars.) Rumours suggest Nissan has been known to offer incentives to get people to keep their lease-returns rather than take them back, and there are stories of even Teslas getting low prices at auction, though in the retail market they have actually done pretty well.

The Leaf is the most popular electric car, and only it and the Tesla are real market cars from big players. The other cars are all “compliance” cars, made by companies who must meet quotas of green vehicles. The 2015 Leaf has a cited range around 80 miles, and users report a real range on the highway closer to 60 miles. For me, that means a car that can’t take me to San Francisco and back. The Leaf would handle a large fraction of my trips around Silicon Valley, but not being able to go to SF is a major detriment in this town. So I decided not to get a 2015 Leaf.

Better cars keep getting pre-announced

That decision was magnified when Nissan announced the 2016 Leaf would be able to do 107 miles. Technically, that’s enough for the San Francisco trip, though in reality it’s just on the edge. Any charging would allow the trip, including a 5 minute (“gas pump” level) stop at a DC supercharger (if nobody else is using it.) So I was waiting for that car to come out when…

They announced the Chevy Bolt, a $30K car (after rebate) with a 200 mile range. Finally a reasonably priced car with enough range. And them rumours circulated of a similar range in the 2017 Leaf. So why buy a 2016 car that can barely make it when something better is coming soon?

Of course, in a year, something even more appealing than the Bolt will be announced. While the Bolt’s range is enough for 99% of my drives (leaving out only Lake Tahoe and road tripping) there is still much that can improve — other parts of the car, the electronics, and of course the battery pack getting even cheaper at that range.

Every year, cars get a little bit better, but we’re in for a period of about 5 years in electric cars where each new year is a lot better, and that’s trouble for people trying to sell them if the customers figure that out. A cell phone is cheap enough to throw out after 2 years. A car is not. To top it off, in a few years the robocar features will start getting more serious (starting with the first no-supervision traffic jam assist) and so other parts of the car will also be on the Moore’s Law curve.

Robocar answer

Robocars have an answer to this rapid depreciation. If they are used as taxis, they can survive. The typical New York Taxi drives 62,000 miles each year and wears out in 5 years. Personal cars take 19 years to wear out, and go around 200,000 miles. Robotaxis will wear out an be scrapped after just 5 years, which means it is less of a burden when they are 4 years old and obsolete from a technology standpoint. (We may also design these vehicles to make it easy to give them hardware upgrades so their electronics can keep pace.)

Personal robocars have it harder. Your 4 year old personal vehicle is going to look like crap compared to the new ones. It will get software updates to match them (which is vital) but without hardware updates it will, like an old iPhone, no longer even be able to handle the software updates. If you buy a personal robocar, get one where it’s easy to swap out the hardware, and expect to pay the cost of this.

Wear and tear of electric cars

The battery is the lifeblood of the electric car. No matter how new the rest is, a reduced range is a deal-killer for most buyers. Indeed, some predictions say the rest of the power train should wear out more slowly than traditional cars, so the depreciation is unfair in some ways.

Battery swap is an option on some electric cars, but that’s a big cost to pay over what you planned to pay. Older battery packs will still work, but deliver less range. Owners will salivate for new packs that are cheaper, lighter, fresher and possibly even higher capacity than what they have. That’s all good, but if you buy an electric car with a pack only good for 4 years at today’s prices, you’ve lost all the economies the electric car hopes to give you. Of course robocars, and especially robotaxis, can manage their batteries for much longer life.

It might make sense to buy a 2012 Leaf for $8,000 and pay $5K to add a battery pack to it that’s brand-new, giving you a car close to matching a new one in certain ways.

With all this, why look at electric cars today? For me, my electricity bill would actually go down due to metering differences, and of course my gasoline bill would drop too. And they are zippy, fun to drive, and quite green with California’s (relatively) green energy grid. And because of this depreciation, used ones are a major bargain. The buyers of new cars (and the federal government) took the hit on a new electric, but you can pick up a 2012 Leaf for $8,000. That’s because all those 2012 units are coming off their leases, and people want them a lot less with those fancier models out there. (In addition, it is known the 2012 had some battery life issues fixed in 2013.)

A lot of people are leasing electric cars. Leasing has one financial advantage (you pay sales tax only on the depreciation you take, rather than the whole car) and otherwise it’s a bad idea unless you’re sure the vendor has guessed badly on the residual value of the car after the lease. With electric cars, you take so much of the depreciation that the tax advantage is not so great. But many electric owners are leasing. The $2500 tax credit in California can often pay for the downpayment, making it easy to come up with the money, and owners are, with good reason, willing to let the vendor take the risk on battery decay and mega-depreciation. Vendors are not idiots, though, and so their residual values are low, but perhaps not low enough. Of course, if you know better cars are coming and are sure you only want the car for 2 years, leasing can ease your legwork.

On the other hand, you can sometimes take over the lease of another electric car owner, letting them suffer the “due at signing” downpayment (which often exceeds all the monthly payments on a short lease) and giving you a car for a very short time, which might be a wise choice with all the new vehicles coming down the pipe.

This post originally appeared on robocars.com.

The first is “simulated contacts” — times when a safety driver intervened, and the vehicle would have hit something without the intervention:

There were 13 [Simulated Contact] incidents in the DMV reporting period (though 2 involved traffic cones and 3 were caused by another driver’s reckless behavior). What we find encouraging is that 8 of these incidents took place in ~53,000 miles in ~3 months of 2014, but only 5 of them took place in ~370,000 miles in 11 months of 2015.
(There were 69 safety disengages, of which 13 were determined to be likely to cause a “contact.”)

The second is detected system anomalies:

There were 272 instances in which the software detected an anomaly somewhere in the system that could have had possible safety implications; in these cases it immediately handed control of the vehicle to our test driver. We’ve recently been driving ~5300 autonomous miles between these events, which is a nearly 7-fold improvement since the start of the reporting period, when we logged only ~785 autonomous miles between them. We’re pleased.

Let’s look at these and why they are different and how they compare to humans.

The simulated contacts are events that would have been accidents in an unsupervised or unmanned vehicle, which is serious. Google is now having one once every 74,000 miles, though Urmson suggests this rate may not keep going down as they test the vehicle in new and more challenging environments. But as noted, a few were not the fault of the system. Indeed, for the larger set of 69 safety disengagements, the rate of those is actually going up, with 29 of them in the last 5 months reported.

How does that number compare? Well, regular people in the USA report about 6 million accidents per year to the police, which means about once every 500,000 miles. But for some time, insurance companies have said the number is twice that, or once every 250,000 miles. Google’s own new research suggests even more accidents are taking place that go entirely unreported. For example, how often have you struck a curb, or even had a minor touch in a parking lot that nobody else knew about? Many people would admit to that, and altogether there are suggestions the human number for a “contact” could be as bad as one in 100,000 miles.

This would put the Google cars at close to the same level, though this is from driving in California with no snow and easy driving conditions. In other words, there is still some way to go, but at least one goal seems to be within in striking distance. Google even reports going 230,000 miles from April to November of last year without a simulated contact, a cherry-picked stretch that nonetheless matches human levels.

People often ask “Which is the biggest obstacle to robocar deployment: technology or regulation?” The answer I give is: neither.

The biggest obstacle in my view is testing. We have to figure out just how to test these vehicles so we can know when a safety goal has been met, and we also have to figure out what the safety goal is.

Various suggestions have been made for what the safety goal should be: for example, having a safety record that matches humans; or one that is twice, 10 times or even 100 times as good as humans. Those higher stretch goals will become good targets, but for now the first question is how to get to the level of humans.

One problem is that the way humans have accidents is quite different from how robots have them. Human accidents sometimes have a single cause (such as falling asleep at the wheel) but many arise because two or more things went wrong. Almost everybody I talk to will agree a time has come when they were looking away from the road to adjust the radio or even play with their phone, and they looked up to see traffic slowing ahead of them, and quickly hit the brakes just in time, resulting in no accident. Accidents often happen when luck like this runs out. Robotic accidents will probably mostly come from one cause. Robots doing anything unsafe — even for a moment — will be cause for alarm, and the source of the error will be fixed as quickly as possible.

Safety anomalies

This leads us to look at the other number — a safety anomaly. At first, this sounds more frightening. They range from 39 hardware issues and anomalies to 80 “software discrepancies”, which may include rarer full-on “blue screen” style (if the cars ran Windows, which they don’t) crashes. People often wonder how we can trust robocars when they know computers can be so unreliable. (The most common detected fault is a perception discrepancy. It is not reported, but I will presume these include strange sensor data or serious disagreement between different sensors.)

It’s important to note the hidden message. These “safety anomaly” interventions did not generally cause simulated contacts. With human beings, the fact that you zone out, take your eyes off the road, text or even in many cases even briefly fall asleep does not always result in a crash for humans, and nor will similar events for robocars. In the event of a detected anomaly, one presumes that independent (less capable) backup systems will immediately take over. Because they are less capable, they might cause an error, but that should be quite rare.

As such, the 5300 miles between anomalies, while clearly in need of improvement, may also not be a bad number. Certainly many humans have such an “anomaly” that often (about every 6 months of human driving.) It depends how often such anomalies might lead to a crash, and what severity of crash it would be.

The report does not describe something more frightening — such as a problem with the system that it does not detect. This is the sort of issue that could lead to a dangerous “careen into oncoming traffic” style event in the worst case scenario. (While I worked on Google’s car a few years ago, I have no inside data on the performance of the current generations of cars.)

I have particular concern with the new wave of projects hoping to drive with trained machine learning and neural networks. Unlike Google’s car and most others, the programmers of those vehicles have only a limited idea how the neural networks are operating. It’s harder to tell if they’re having an “anomaly,” though the usual things like hardware errors, processor faults and memory overflows are of course just as visible.

The other vendors

Google didn’t publish total disengagements, judging most of them to be inconsequential. Safety drivers are regularly disengaging for lots of reasons:

• Taking a break, swapping drivers or returning to base
• Moving to a road the car doesn’t handle or isn’t being tested on
• Any suspicion of a risky situation

The latter is the most interesting. Drivers are told to take the wheel if anything dangerous is happening on the road, not just with the vehicle. This is the right approach — you don’t want to use the public as test subjects. You wouldn’t say, “Let’s leave the car auto-driving and see what it does with that group of schoolchildren jaywalking.” Instead the approach is to play out the scenario in a simulator and see if the car did the right thing.

Delphi reports 405 disengagements in 16,600 miles — but their breakdown suggests only a few were system problems. Delphi is testing on highways where disengagement rates are expected to be much lower.

Nissan reports 106 disengagements in 1485 miles, most in their early stages. For October-November, their rate was 36 for 866 miles. They seem to be reporting the more serious ones, like Google.

Tesla reports zero disengagements, presumably because they define their vehicle as not having a fully autonomous mode.

VW’s report is a bit harder to discern, but it suggests 5500 total miles and 85 disengagements.

Google’s lead is overwhelming.

How safe do we have to be?

If the number is the 100,000 mile or 250,000 mile number we estimate for humans, that’s still pretty hard to test. You can’t just take every new software build and drive it for a million miles (about 25,000 hours) to see if it has fewer than 4 or even 10 accidents. You can and will test the car over billions of miles in simulator, encountering every strange situation ever seen or imagined. Before the car has an accident it will be unlike a human. It will probably perform flawlessly. If it doesn’t, that will be immediate cause for alarm and correction of the problem.

Makers of robocars will need to convince themselves, their lawyers and safety officers, their boards, the public and eventually even the government that they have met some reasonable safety goal.

Over time we will hopefully see even more detailed numbers on this. That is how we’ll answer this question.

This does turn out to be one advantage of the supervised autopilots, such as what Tesla has released. Because it can count on all the Tesla owners to be the fail-safe for their autopilot system, Tesla is able to quickly gather a lot of data about the safety record of its system over a lot of miles. Far more than can be gathered if you have to run the testing operation with paid drivers or even your own unmanned cars. This ability to test could help the supervised autopilots get to good confidence numbers faster than expected.

Indeed, though I have often written that I don’t feel there is a good evolutionary path from supervised robocars to unmanned ones, this approach could make my prediction be in error. For if Tesla or some other car maker with lots of cars on the road is able to make an autopilot, and then observe that it never fails in several million miles, then they might have a legitimate claim on having something safe enough to run unmanned, at least on the classes of roads and situations in which the customers tested it on. Though a car that does 10 million perfect highway miles is still not ready to bring itself to you door to door on urban streets, as Elon Musk yesterday claimed would happen soon with Tesla.

This post originally appeared on robocars.com. If you liked this post, you may also enjoy:

• California’s new DMV regulations may kill the state’s robocar lead
• Why Tesla’s Autopilot and Google’s car are entirely different animals
• When do robocars become cheaper than standard cars? Automated Vehicle Symposium recap (Part 1)
• Why Tesla’s purported liability ‘fix’ is technically and legally questionable
• Issues in regulating robocars, and the case for a light hand

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Needing a driver

The draft operating rules effectively forbid Google’s current plan, making it illegal to operate a vehicle without a licensed and specially certified driver on board who is ready to take control. Google’s research led them to feel that having a transition between a human driver and software is dangerous, and that the right choice is a vehicle with no controls for humans. Most car companies, on the other hand, are attempting to build “co-pilot” or “autopilot” systems in which the human still plays a fundamental role.

The state proposes banning Google-style vehicles for now, and drafting regulations on them in the future. Unfortunately, once something is banned, it is remarkably difficult to un-ban it. That’s because nobody wants to be the regulator or politician who un-bans something that later causes harm that can be blamed on them. And these vehicles will cause harm — just less harm than the people currently driving are doing.

The law forbids unmanned operation, and requires the driver/operator to be “monitoring the safe operation of the vehicle at all times and be capable of taking over immediate control.” This sounds like it certainly forbids sleeping, and might even forbid engrossing activities like reading, working or watching movies.

Special certificate

Drivers will be required to have more than just a licence. They will also be required to have a certificate showing they are trained in operating a robocar. On the surface, that sounds reasonable, especially since hand-off driving has dangers that training could reduce. But in practice, it bring a number of unintended consequences:

• Rental or even borrowing such vehicles becomes impossible without a lot of preparation and some paperwork by the person trying it out.
• Out of state renters may face a particular problem as they can’t have California licences. (Interstate law may — bizarrely — let them get by without the certificate while Californians would be subject to this rule.)
• Car sharing or delivered car services (like my “whistlecar” concept or Mercedes Car2Come) become difficult unless sharers get the certificate.
• The operator is responsible for all traffic violations. Even though several companies have said they will take responsibility, they can only take financial responsibility, and won’t be able to help you with points on your licence or criminal liability, rare as that is. People will be reluctant to assume that responsibility for things that are the fault of the software in the car they use, as they have little ability to judge that software.

No robotaxis

With no robotaxis or unmanned operation, a large fraction of the public benefits of robocars are blocked. All that’s left is the safety benefit for car owners. This is not a minor thing, but it’s a small a part of the whole game (and active safety systems can attain a fair chunk of it in non-robocars.)

The state says it will write regulations for proper robocars that are able to run unmanned. But it doesn’t say when those will arrive, and unfortunately any promises about that will be dubious and non-binding. The state was very late with these regulations — perfectly understandable since not even vendors know the final form of the technology — and it may well be late again. Unfortunately, there are political incentives for delays, perhaps even with indeterminate delays.

This means vendors will be uncertain. They may know that someday they can operate in California, but they can’t plan for it. With other states and countries around the world chomping at the bit to get vendors to move their operations, it will be difficult for companies to choose California, even though so far most of them have.

People already in California will continue their R&D in California, because it’s expensive to move such things, and Silicon Valley retains its attraction as the high-tech capital of the world. But they will start making plans for first operation outside California, in places that have an assured timetable.

It will be less likely that somebody would move operations to the state because of the uncertainty. Why start a project in California — which in spite of its advantages is also the most expensive place to operate — without knowing when you can deploy there? Companies will deploy close to home if they have the option.

It might be that the car companies whose prime focus is on co-pilot or autopilot systems today may not be bothered by this uncertainty. In fact, it’s good for their simpler early goals because it slows the competition down. But most of them have also announced plans for real self-driving robocars where you can act just like a passenger. Their teams all want to build them. They might enjoy a breather, but in the end, they don’t want these regulations either.

The new regulations also mean that delivery robots like the ones we are making at Starship won’t be able to go on the roads, and will have to stick to the sidewalks. This could block development because the test vehicles will be required to have a human safety driver with a physical steering system. Requiring that driver makes sense for passenger cars, but is impossible for a robot the size of breadbox.

California should — after receiving comment — alter these regulations. They should allow unmanned vehicles that meet appropriate functional safety goals to operate, and they should have a real calendar date when this is going to happen. If they don’t, they won’t be helping to protect Californians. They will take California from being the envy of the world as the place that has attracted robocar development from all around the planet to just another contender. And that won’t just cost jobs, it will delay the deployment in California of a technology that will save the lives of Californians.

I don’t want to pretend that deploying full robocars is without risk. Quite the reverse, people will be hurt. But people are already being hurt by human drivers, and the strategy of taking no risk is the wrong one.

A version of this post originally appeared on robocars.com.

Tesla’s autopilot isn’t even particularly new. Several car makers have had similar products in their labs for several years, and some have released it to the public, at first in a “traffic jam assist” mode, but reportedly in full highway cruise mode outside the USA. The first companies to announce it were Cadillac with the “Super Cruise” and VW’s “Temporary Autopilot” — but they delayed that until much later.

Remarkably, Honda showed off a car ten years ago doing this sort of basic autopilot (without lane change) and sold only in the UK. They decided to discontinue the project, however. That this was actually promoted as an active product ten years ago will give you some clue as to how different this was from the bigger efforts.

Cruise products like these require constant human supervision. With regular cruise control, you could take your feet off the pedals, but might have to intervene fairly often either by using the speed adjust buttons or full control. Interventions could be several times a minute. Later came “Adaptive Cruise Control”, which still required you to steer and fully supervise, but would rarely require intervention on the pedals while driving on the highway — a few times an hour might be acceptable.

The new autopilot systems allow you to take your hands off the wheel but demand full attention. Users report needing to intervene rarely on some highways, but frequently on other roads. Once again, if you only need to intervene once an hour, the product could make your drive more relaxing.

Now consider a car that drives without supervision …

Human drivers have minor accidents about every 2,500 to 6,000 hours, depending on what figures you are using — that would be about once every 10 to 20 years of driving. A fatal accident takes place every 2,000,000 hours of driving — about once every 10,000 years for the typical driver, thankfully a much longer span than a person’s lifetime.

If a full robocar needs human intervention, logic tells you that it’s going to have an accident because there is nobody there to intervene. Just like with human drivers, most of the errors that would cause an accident are minor: running off the road, fender benders. Not every mistake that could cause a crash or a fatality causes one. Indeed, humans make mistakes that might cause a fatality far more often than every 2,000,000 hours, because we “get away” with many of them.

Even so, the difference is staggering. A cruise autopilot (such as Tesla’s) is a workable product if you have to correct it a few times an hour, whereas a full robocar product is only workable if you need to correct it only after decades or even lifetimes of driving. This is not a difference of degree, it is a difference of kind. It is why there is probably not an evolutionary path from the cruise/autopilot systems based on existing ADAS technologies to a real robocar. Doing many thousands times better will not be done by incremental improvement. It almost surely requires a radically different approach, and probably very different sensors.

To top it all off, a full robocar doesn’t just need to be great at avoiding accidents. If it’s running unmanned, with no human to help it at all, it needs a lot of other features and capabilities too.

The mistaken belief in an evolutionary path also explains why some people imagine robocars are many decades away. If you wanted evolutionary approaches to take you to 100,000x better, you would expect to wait a long time. When an entirely different approach is required, what you learn from the old approach doesn’t help you predict how the other approaches — including unknown ones — will do.

It does teach you something, though. By simply being on the road, Tesla will encounter all sorts of interesting situations its developers weren’t expecting, and they will use this data to train new generations of software that do better. They will learn things that help them make the revolutionary unmanned product they hope to build in the 2020s. This is a good thing.

Google and others have also been out learning that, and soon more teams will.

This post originally appeared on Robocars.com.

If you liked this article, you may also be interested in:

• The two cultures of robocars
• Might the first, supervised robocars be… well… boring?
• Issues in regulating robocars, and the case for a light hand
• Future driving: Business models for robocars

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

In a statement released yesterday, Volvo said it would accept full liability whenever one of its cars is in autonomous mode.

Among the most common questions the mainstream press asks about robocars is “Who is going to be liable in a crash?” but an answer is seldom provided. I have often joked that we should change question to “Who gets sued?” because the answer to that question (at least in the US) is easy: “Everybody will get sued.”

But in spite of this hard and central question, the long term answer has always been obvious. If the software/hardware in a car is responsible for the crash (ie. caused the vehicle to do something like depart its right-of-way), then it’s obvious that the vendor of that car will be liable, or perhaps some proxy for the vendor, such as a taxi fleet operator.

Volvo has now declared this to be the case for them and Google has said it as well. Good for them.

The main reason this has sat as an open question for so long is that any lawyer will tell you never to admit in advance that you should be liable for something. From a lawyer’s standpoint, that can never do anything but come back to haunt you later. There’s no upside, and a big downside, so they tell clients not to do it.

It is not just a legal decision. After all, customers are not going to want to buy or even ride in cars if the rule is, “If this car crashes because of our bug, then you (or your insurance) will be liable, and demerit points or even criminal charges might go to you.”

Early adopters might accept that, but it’s not a workable long-term policy. If the question of points and rare criminal charges could be eliminated, we could see a workable system where the passenger is liable and has insurance to fully cover it, but deep down that’s a silly system; again something only for the early days.

Even if you could get such a system in place with passenger-insurance, the reality is the vendor would still get sued. Even a great policy and indemnification from the passenger would not prevent plaintiff’s lawyers from wanting to go after the deep pocketed vendor. They would look for the hope of negligence (or in their dreams, VW style fraud) to get juicy damages. Even if not directly liable, the vendor would pay more in legal costs for some cases than the cost of the accident.

As I have written before, in today’s world, car accident costs are not paid by individuals or even companies. If I’m liable, my insurance company pays, and every policyholder shares the cost in their premiums. If my car has a defect, the car company pays, but builds in a share of that cost (or insurance against it) into the price of every car — once again the public shares the cost.

This will not change in the world of robocars, and fighting over liability is really just fighting over who the money will flow through, and who will get the burdens and benefits of control of the legal strategy.

This saner approach makes the vendor responsible — at least while the car is driving itself — and the vendor self-insuring and getting reinsurance (or product liability insurance) to cover the cost.

The cost we currently know and can handle very well: rooms of actuaries at every auto insurance company study it all day. What we don’t know yet is the cost of the early, special lawsuits that will be unlike typical car crash cases.

If the world is rational, the total number of accidents and their severity will go down, and so will the cost of dealing with them. The world may not be rational, but ideally this new lower cost is built into the cost of the ride or the car, and we all pay less.

For cars that people drive some of the time, traditional insurance will do the job; but it should be billed by the mile — what is called “pay-as-you-drive” or PAYD. I have a more detailed earlier article on the issues around accidents for those seeking more depth.

With the recent Volvo announcement, perhaps we can demote the liability issue from the most-frequent-questions list … though the one I really want removed from it is “What happens when a car has to choose between killing two people?”

This post originally appeared on Robocars.com.

Mercedes at CES 2015.

Yesterday I attended the “Silicon Valley reinvents the wheel” conference by the Western Automotive Journalists, which had a variety of talks and demonstrations of new car technology. Now that robocars have hit the top of the “Gartner Hype Cycle” for 2015, everybody is really piling on, hoping to see what good for the robocar can bring to their industry. And of course, there is a great deal of good, but not for every industry.

Let me break down some potential misconceptions if my predictions are true …

The dashboard almost vanishes

The dashboard of the modern car is amazing and expensive. Multiple digital screens and lots of buttons and interfaces on the wheel, the central stack and beyond. Fancy navigation systems, audio and infotainment systems, mobile apps, phone integration, car information and much more can be found there. There have been experiments with gesture- and speech-based controls, concierge services and fancy experimental controls. There are video screens in the headrests for the rear passengers. In recent years, specialized offerings like Ford Sync, GM OnStar and many others have become differentiating factors in cars, and there’s a lot of money in that dashboard.

This started changing as car companies came to accept the dominance of the mobile phone in the driver’s life. People stopped wanting things like navigation and music from their car. They had better versions of those things in their phone, and they knew how to use them and had customized them. This year we are seeing deployment of “Android Auto” and “Apple CarPlay”— which connect your phone to the car’s dashboard screen, and let you see and control a very limited number of apps on the screen. The carmakers had to be dragged to this kicking and screaming, but frankly today’s offerings from both Google and Apple are fairly poor in this department. For example, you can only run the special approved and modified apps. If you like to navigate with Waze instead of Google Maps (even though Waze is a Google product) you can’t — your phone is locked out when it is driving Android Auto.

All of this is still based on the the idea that the driver must put all focus on driving. You can’t have complex UI where you look at the screen for more than a glance, and you should not distract the driver.

The Mercedes F015 concept car shown here is one of the first automaker explorations of a car where it’s OK to distract the driver. On the doors and walls of this car, you can see large touchscreens with concept apps on them.

In contrast, Google’s new prototype 2-seater car barely has a dashboard at all. Their answer shows more wisdom, I think. Your phone and tablet are always going to be your preferred choice for mobile computer interactions. Access to the internet, music and entertainment will go through them. The phone is updated every two years, or even less, and will always have superior hardware and services, and more effort goes into its design and the design of its systems and apps than will go into a car system. But even if the car system is fantastic, two years later it will be behind the phone.

You can even give commands to the car through your phone, such as what destination to drive to. And we all know it’s vastly easier to enter destinations on phones than in any car nav system we’ve ever seen.

The full-auto robocar becomes more like a living space than a car

More like your TV room or your office. It is those places where we might seek clues as to the interior of the car. My living spaces do not have touchscreens on all the walls, for example, so it’s not too likely my car will either — or if they become useful, both will have them.

In my office I do have a desk and a big screen, along with better user input devices (full keyboard and trackball) as well as more computing power. These are things I will want in my car, but of course they should be personalized to me, probably using my phone as a gateway to that information. In my TV room I do have a large screen, and that will probably show up in the car, but it probably won’t be a touchscreen any more than my TV is. I will control it from my phone.

Car makers agreed that they should not attempt to pioneer new forms of user interface. Experiments in gesture control and other novel UIs have not done well. Drivers don’t want to learn entirely new styles of computer interaction when sitting in a car. They want to use the forms they already know. New forms should be pioneered in the general computing world, and moved into the car.

Failure modes

With so much dependence on the phone, the car will need a small and simple tethered phone so that if your phone is not present for some reason, you can still do all the basic functions. Of course there will be power, so running out of battery is not an issue, other than for unlocking and summoning the car.

We also have to realize that long ago, car dashboards had barely anything on them. Even emergency driving (when the self-drive system has failed, and you plug in handlebars or a joystick) hardly needs anything more than a speedometer, and frankly you can drive fine with other traffic even without that.

Security

It should be noted that the phone is not a secure device. As such, it won’t send much to the self-driving system. It will receive status information from it, but only give very limited commands. Indeed, it is quite possible that your phone might control your car through the cloud, sending commands to a central server that then talks to the car. A more simplified interface must exist for cloud dead zones, with all its traffic highly scrutinized. Robocars will be able to drive without the cloud, but it will be the exception, not the rule, and it won’t be done for very long or far.

Audio

A lot of the conference event dealt with audio. Audio makers are making good strides in producing fantastic car audio, and doing it at high prices. This trend started when it became clear that the car was the primary place to listen to music for many people, though this has decreased with the rise of the smartphone and other good music players.

People will continue to want good audio systems in their cars, but they will be offered a new, and cheaper option thanks to robocars: namely quality noise-cancelling headphones with subwoofer seats. Today, drivers are not allowed to wear noise isolating headphones because they should respond to traffic sounds and sirens. In the future, drivers might like to take themselves away from road noise to get good music. Aside from being cheaper, this solution allows all occupants of a car to have different music (or video soundtrack) if they wish to.

Last weekend I tried out a new product called a SubPac, which is a small seat cushion that emits strong subwoofer bass directly into your body, providing a fair bit of the feeling of standing in from of giant dance club speakers. This unit is $380 today but will come down in price and soon should be a modest extra cost in car seats. (I am also interested in backpack versions which will eventually make silent disco more acceptable to those who crave mind-numbing bass.

In the other direction, the freedom of design that robocars provide means that larger vehicles will become interesting spaces, and you might come to view the car parked in your driveway as your music listening room or even home theatre and video game room, and invest the money in that (if you own your robocar, that is).

Design

Results from a design competition at Academy of Art University of San Francisco were presented at the conference too. The designs were interesting, and in line with many other futuristic car designs I have seen, though larger and with more lounging. Sadly, the focus was on large vehicles.

The reality, I think, is that most new vehicles will be small, one- or two-person vehicles, and I would like to see more radical design ideas there. Around 80% of urban trips are solo, and we have a tremendous opportunity to design comfortable higher end solo vehicles that are very efficient in terms of both energy and road space occupancy. Here’s where we want to see adjustable comfortable chairs, fold out desks and screens, and other techniques to give us the things from our offices and homes in a space just 1.5m wide.

Roads

The concept of infrastructure changes were touched on at the conference as well. Regular readers here will know I instead favour minimal infrastructure change and the idea of “virtual infrastructure,” where as much is done at software levels as possible. In other words, “smart cars on stupid roads.” In spite of this, cities and agencies commonly ask what they should do to hasten the robocar, and while I love the sentiment, the reality is that the answers are minimal.

Conclusions

All of this takes us further towards the conclusion that some future models of the robocar will be incredibly cheap. Particularly the “city cars” that never go on the highway and only carry one or two people. With simple electric drivetrains they will be easy to build and maintain, and eventually their battery cost will become very reasonable. The dashboard vanishes along with many other controls. The expensive sound system vanishes. The windshields need not be a large custom piece of curved glass — in fact they don’t even have to exist other than for passenger comfort. The parts count goes down significantly. The only additions are the sensors and computers, both on Moore’s law downward curves, and perhaps that nice large screen for working. We may also see a more advanced computer-controlled suspension to keep the ride smooth and comfortable.

This post originally appeared on Robocars.com.

Hear about the current state of the driverless vehicle industry from experts including IEEE Member Jeffrey Miller, IEEE Fellow Wei-Bin Zhang, Bernard Soriano, and Bryant Walker Smith. In addition to present-day commentary, the panelists explored the future of the industry as it relates to technology, policy and ethics. The roundtable discussion, which was broadcast live on August 28, was moderated by Justin Pritchard of the Associated Press.

In a surprise move today, Toyota held a press conference (see video below) announcing a substantial investment in robotics and AI research to develop “advanced driving support” technology, with former Program Manager of DARPA’s DRC Gill Pratt directing the overall project as Executive Technical Advisor. Toyota will allocate USD$50M over the next five years in a partnership with MIT’s CSAIL (headed by Daniela Rus) and Stanford’s SAIL (headed by Fei-Fei Li) to develop research facilities in Stanford and Cambridge.

Two main themes from the press conference underline the tone of this effort:

First,  Toyota doesn’t refer to autonomous cars but to ‘advanced driving support’ . And this makes sense because, unlike Google (with its fully autonomous car), Toyota’s origins are in car manufacturing and not in software development. Car manufacturers have for some time been introducing ever rising levels of autonomy in their cars and Toyota is following that vector. There are legal advantages to having the driving be ultimately responsible for the car’s movements and therefor any possible accidents; when a vehicle doesn’t respond as expected, a costly product liability suit could follow, as Toyota well knows. Thus what appears at one level to be a more conservative approach to autonomy on the road may in essence be a way to bypass potential legal nightmares and bring autonomous driving technology to the market sooner.

Kiyotaka Ise, Toyota Motor Corporation Senior Managing Officer, repeatedly stated hybrid cars as an example of a specific technology that Toyota pioneered (along with Honda) and brought to market even while it was borderline unprofitable due to its large cost at the time. This offers a possible hint of what may follow.

The second interesting point made clear during Toyota’s Q&A is that this project is only part of the auto manufacturer’s investment in the field. $50M is a lot of money, but for a profitable car maker the size of Toyota, it isn’t much. It looks like Toyota wants to establish a strong presence in the US by collaborating with two of the best research centers in the world (the budget will be equally split between the two universities) … but being a truly global company, it is developing other projects elsewhere as well. It’s worth mentioning that almost all large car manufacturers have similar research projects, and they usually collaborate with research institutes, universities or with each other.

In this respect, Toyota’s decision to bring on Pratt as the project lead appears astute. Pratt’s high visibility role as Program Manager of DARPA’s DRC has left him with significant experience managing and promoting large, big-budget projects, and has provided him with deep connections into US research labs.

You can read Toyota’s press release here for all the details. Automaton has also an excellent coverage of the story (see Toyota Announces Major Push Into AI and Robotics, Wants Cars That Never Crash) and says they will be releasing more in-depth stories about the deal early next week. You can watch the full press conference below:

Most public discussion of robocars has depicted them as costing much more than regular cars. That’s because the cars built to date have been standard cars modified by placing expensive computers and sensors on them. Many cars use the $75,000 Velodyne LIDAR and the similarly priced Applanix IMU/GPS, and most forecasts and polls have imagined the first self-driving cars as essentially a Mercedes with $10,000 added to the price tag to make it self driving. After all, that’s how things like Adaptive Cruise Control and the like are sold.

Google is showing us an interesting vision with their 3rd generation buggy-style car. That car has no steering wheel, brakes or gas pedal, and it is electric and small. It’s a car aimed at “Mobility on Demand.”

When people ask me “How much extra will these cars cost?” my usual answer has been that while the cars might cost more, they will be available for use by the mile, where they can cost less per mile than owning a car does today. In other words, overall it will be cheaper. That’s in part because of the savings from sharing, and having vehicles go more miles in their lifetime. More miles in the life of a car at the same cost means a lower cost per mile, even if the car costs a little more.

The sensors cost money, but that cost is already in serious decline. We’re just a few years away from $250 LIDARs and even cheaper radar. Cameras are already cheap, and there are super cheap IMUs and GPSs already getting near the quality we need. Computers of course get cheaper every year.

This means that  we are not too far from when the cost of the sensors is less than the money saved by what you take out of the car. After all, a steering wheel, gas and brakes cost money. Side mirrors cost money (ever had to replace them?). That fancy dashboard with all its displays and controls costs a lot of money, but almost everything it does in a robocar can be done by your tablet.

That said, you need a few extra things in your robocar: two steering motors and two braking systems, some more short range sensors and a cell phone radio.

But there’s even more you can save, especially with time. Because mobility on demand means you can make cars that are never used for anything but short urban trips (the majority of trips, as it turns out) you can save a lot more money on those cars. These cars need not be large or fast. They don’t need acceleration. They won’t ever go on the highway, so they don’t need to be safe at 60mph. Electric drive, as we discussed earlier, is great for these cars, and electric cars have far fewer parts than gasoline ones. Today, their batteries are too expensive, but everything else in the car is cheaper, so if you solve the battery cost using the methods I outlined in my previous post, we’re saving serious money. And small one- or two-person cars are inherently cheaper to boot.

Of course, you need to make highway cars, and long-range 4WD SUVs to take people skiing. But these only need be a fraction of the cars, and people who use a mix of cars will see a big saving.

For a long time, we’ve talked about some day also removing many of the expensive safety systems from cars. When the roads become filled with robocars, you can start talking about having so few accidents you don’t need all the safety systems, or the 1/3 of vehicle weight that is attributable to passive safety. That day is still far away, though cars like the Edison2 Very-Light-Car have done amazing things even while meeting today’s crash tests. Companies like Zoox and other startups have pushed visions of completely redesigned cars, some of them at lower cost for a while. But this seems like it might become true sooner rather than later.

Evacuation in a hurricane

One participant asked how, if we only had 1/9th as many cars (as some people forecast, I suspect it’s closer to 1/4) we would evacuate sections of Florida or similar places when a hurricane is coming. I think the answer is a very positive one — simply enforce car pooling / ride sharing during the evacuation. While there is not a lot I think policymakers should do at this time, some simple mandates could help a lot in this arena. While people would not be able to haul as much personal property, it is very likely there would be more than enough seats available in robocars to evacuate a large population quickly if you fill all the seats in cars going out. Further, those cars can go back in to get more people if need be.

Filling those seats would actually get everybody out faster, because there would be far less traffic congestion and the roads would carry far more people per hour. In fact, that’s such a good idea it could even be implemented today. When there’s an evacuation, require all to use an app to register when they are almost ready to leave. If you have spare seats, you could not leave (within reason) until you picked up neighbours and filled the seats. With super-carpooling, everybody would get out very fast on much less congested roads. Those crossing the checkpoint on the way out with empty seats would be photographed and ticketed unless the app allowed them to leave like that, or the app records that it tried to reach the server and failed, or other mitigating circumstances. (This is all hours before the storm, of course, before there is panic, when people will do whatever they can.) Some storms might be so bad the cars are at risk. In that case, if the road capacity is enough, people could move out all the cars too, to protect them. But in most cases, it’s the people that are the priority.

More soon as the conference gets underway.

Photo credit: John Eckman via Flickr.

A reader recently asked about the synergies between robocars and ultracapacitors / supercapacitors. It turns out they are not what you would expect, and it teaches some of the surprising lessons of robocars.

Ultracapacitors (ultracaps) are electrical storage devices — like batteries — that can be charged and discharged very, very quickly. That makes them interesting for electric cars, because slow charging is the bane of electric cars. They also tend to support a very large number of charge and discharge cycles: they don’t wear out the way batteries do. Where you might get 1,000 or so cycles from a good battery, you could see several tens of thousands from an ultracap.

Today, ultracaps cost a lot more than batteries. LIon batteries (like in the Tesla and almost everything else) are at $500/kwh of capacity and falling fast — some forecast it will be $200 in just a few years, and it’s already cheaper in the Tesla. Ultracaps are $2,500 to $5,000 per kwh, though people are working to shrink that.

They are also bigger and heavier. They are cited as just 10 wh/kg and on their way to 20 wh/kg. That’s really heavy — LIon are an order of magnitude better at 120 wh/kg and also improving.

So with the ultracap, you are paying a lot of money and a lot of weight to get a super-fast recharge. It’s so much money that you could never justify it if not for the huge number of cycles. That’s because there are two big money numbers on a battery — the $/kwh of capacity — which means range — and the lifetime $/kwh, which affects your economics. Lifetime $/kwh is actually quite important but mostly ignored because people are so focused on range. An ultracap, at 5x the cost but 10x or 20x the cycles actually wins out on lifetime $/kwh. That means that while it will be short range, if you have a vehicle that is doing tons of short trips between places it can quickly recharge, the ultracap can win on lifetime cost, and on wasted recharging time, since it can recharge in seconds, not hours. That’s why one potential application is the shuttle bus, which goes a mile between stops and recharges in a short time at every stop.
How do robocars change the equation? In some ways it’s positive, but mostly it’s not.

• Robocars don’t mind going out of their way to charge, at least not too far out of their way. Humans hate this. So you don’t need to place charging stations conveniently, and you can have a smaller number of them.
• Robocars don’t care how long it takes to charge. The only issue is they are not available for service while charging. Humans on the other hand won’t tolerate much wait at all.
• Robocars will eventually often be small single-person vehicles with very low weight compared to today’s cars. In fact, most of their weight might be battery if they are electric.
• Users don’t care about the power train of a taxi or its energy source. Only the fleet manager cares, and the fleet manager is all about cost and efficiency and almost nothing else.

Now we see the bad news for the ultracap. It’s main advantage is the fast recharge time. Robots don’t care about that much at all. Instead, the fleet manager does care about the downtime, but the cost of the downtime is not that high. You need more vehicles the more downtime you have during peak loads, but as vehicles wear out by the km, not the year, the only costs for having more vehicles are the interest rate and the storage (parking) cost.

The interest cost is very low today. Consider a $20,000 vehicle. At 3%, you’re paying 7 cents/hour in interest. So 4 hours of recharge downtime (only at peak times when you need every vehicle) doesn’t cost very much, certainly not as much as the extra cost of an ultracap. The cost of parking is actually much more, but will be quite low in the beginning because these vehicles can park wherever they can get the best rate and the best rate is usually zero somewhere not too far away. That may change in time, but it’s how it is today in most places.

In addition to the high cost, the ultracap comes with two other big downsides. The first is the weight and bulk. Especially when a vehicle is small and its weight comes mostly from its battery, adding 200kg of battery actually backfires; you get diminishing returns on adding more in such vehicles. The other big downside is the short range. Even with the fast recharge time, you would have to limit these vehicles to doing only short cab hops in urban spaces of just a few miles, sending them off after just a few rides to get a recharge.

A third disadvantage is you need a special charging station to quick charge an ultracap. While level 2 electric car charging stations are in the 7-10kw range, and rapid chargers are in the 50kw-100kw range, ultracap chargers want to be in the megawatt or more range, and that’s a much more serious proposition, and a lot more work to build them.

The fact that robocars don’t need fast refuelling in convenient locations opens up all sorts of energy options. Natural gas, hydrogen, special biofuels and electricity all become practical even with gasoline’s 100-year headstart when it comes to deployment and infrastructure, and even sometimes in competition with gasoline’s incredible convenience and energy density. But what the robocar brings is not always a boon to every different form of energy storage.

Still, for convenience, the first robocars will probably be gasoline and electric.

This article originally appeared on robocars.com.

Earlier this week Baidu announced they will be collaborating with BMW to launch an autonomous vehicle prototype later this year.

Here’s a roundup of news stories in the field of robocars.

Google begins PR campaign and talks about accidents

As the world’s most famous company, Google doesn’t need to seek press and the Chauffeur project has kept fairly quiet, but it just opened a new web site that will feature monthly reports on the status of the project. The first report gives details of all the accidents in the project’s history, which I’ve discussed previously. A new one just took place in the last month, but like the others, it did not involve the self-driving software. Google’s cars continue to not cause any accidents, though they have been at the receiving end of a modestly high number of impacts, perhaps because they are a bit unusual.

The zero at-fault accident number is both impressive, and possibly involves a bit of luck. Perhaps it even raises unrealistic expectations of perfection, because I believe there will be at-fault accidents in the future for both Google and other teams. Most teams, when they were first building their vehicles, had minor accidents where cars hit curbs or obstacles on test tracks, but the track records of almost all teams since then are surprisingly good. One way that’s not luck, of course, is the presence of safety drivers ready to take the controls if something goes wrong. They are trained and experienced, though some day, being human, some of them will make a mistake.

Baidu to build a prototype

In November I gave a “Big Talk” for Baidu in Beijing on cars. Perhaps there is something about search engines because they have made announcements about their own project. Like Google, Baidu has expertise in mapping and various AI techniques, including the advice of Andrew Ng, whose career holds many parallels to that of Sebastian Thrun, who started Google’s project. (Though based on my brief conversations with Andrew I don’t think he’s directly involved.)

Virginia opens test roads

The state of Virginia has designated 70 miles of roads for robocar testing. That’s a good start for testing by those working in that state, but it skirts what to me is a dangerous idea — the thought that there would be “special” roads for robocars designated by states or road authorities. The fantastic lesson of the DARPA Grand Challenges was the idea that the infrastructure remains stupid and the car becomes smart, so that the car can go anywhere once its builders are satisfied it can handle that road. So it’s OK to test on a limited set of roads but it’s also vital to test in as many situations as you can, so you need to get off that set of roads as soon as you can.

Zoox startup un-stealthed

Zoox is probably the first funded startup working on a real, fully automated robocar. They were recently funded by DFJ ventures and set up shop in rented space at the SLAC linear accelerator lab. Zoox was begun by Tim Kentley-Klay, a designer and entrepreneur from Australia, and he later joined forces with Jesse Levinson, a top researcher from Stanford’s self-driving car projects.

I’ve known about Zoox since it begain and had many discussions. They first got some attention a while back with Tim’s designs, which are quite different from typical car designs, and presume a fully functional robocar — the designs feature no controls for the humans, and don’t even have a windshield to see forward in some cases. (Indeed, they don’t have a “forward” since an essential part of the design is to be symmetrical and move equally well in both directions, avoiding the need for some twists and turns.) I like many elements of the Zoox vision, though in fact I think it is even more ambitious than Google’s, at least from a car design standpoint, which is quite audacious in a world where most of the players think Google is going too far.

You can see details in this report on Zoox from IEEE. I haven’t reported on Zoox under FrieNDA courtesy — in fact the early consultations with “Singularity University” described in the article are actually discussions with me.

Zoox is not the first small startup. Kyle Haight’s “Cruise” has been at it a while aiming at a much less ambitious supervised product, and truck platooning company Peleton has even simpler goals, but expect to see more startups enter the fray and fight with the big boys in the year to come.

Mercedes E Class

Speaking of supervised cruising, the report is that the 2016 Mercedes E Class will offer highway speed cruising in the USA. This has been on offer in Europe in the past. As I wrote earlier, I am less enthused about supervised cruising products and think they will not do tremendously well. Tesla’s update to offer the same in their cars will probably get the most attention.

Non-Stories

The press continue to get super excited about things that aren’t real. In spite of many reports, Uber does not yet have a car cruising the streets of Pittsburgh, though there is reality to the report that Uber has “poached” a large fraction of the robotics research crew from CMU.

In addition, many stories reported that Tesla had “solved” the liability problem of robocars through the design of their lane change system. In their system (and in several other discussed designs — they did not come up with this) the car won’t change lanes until the human signals it is OK to do so, usually by something like hitting the turn signal indicator. The Tesla plan is for a supervised car, and in a supervised car all liability is already supposed to go to the human supervisor.

Changing lanes safely is surprisingly challenging, because there is always the chance somebody is zooming up behind you at a rather rapid rate of speed. That’s common merging into a carpool lane, or on German autobahn trips. Most supervised cars have only forward sensing, but to change lanes safely you need to notice a car coming up fast from behind you, and you need to see it quite a distance away. This requires special sensors, such as rear radars, which most cars don’t have. So the solution of having the human check the mirrors works well for now.

More and more stories keep getting excited by “connected car” technology, in particular V2V communications using DSRC. They even write that these technologies are essential for robocars, and it gets scary when people like the transportation secretary say this. I wish the press covering this would take the simple step of asking the top teams who are working on robocars whether they plan to depend on, or even make early use of vehicle to vehicle communications. They will find out the answers will range form “no, not really” to a few vague instances of “yes, someday” from car companies who made corporate support commitments to V2V. The engineers don’t actually think they will find the technology crucial. The fact that the people actually building robocars have only a mild interest, if any, in V2V, while the people who staked their careers on V2V insist it’s essential, should maybe suggest to the press that the truth is not quite what they are told.

This article originally appeared on robocars.com.

Photo source: Wikipedia [Flckr user jurvetson (Steve Jurvetson) CC BY-SA 2.0]

Earlier this month, the Associated Press reported on several past crashes involving automated vehicles. (Per SAE Standard J3016, I use the term “automated vehicle” instead of “autonomous vehicle” or “self-driving car” or “driverless car.”) A few thoughts.

1) As I wrote in 2012, we would need more information — about the crashes themselves, the conditions under which each company’s automated vehicles are tested, and the situations in which each company’s test drivers intervene — to provide statistical context for these incidents.

2) In some ways, the AP’s inquiry gave us a preview of how public and private actors might respond to future automated vehicle crashes that actually result in injury or death. It may be instructive to view the reactions ofGoogle, Delphi, and the California DMV in this light.

3) Over the last few years, I have advised both developers and regulators of automated systems to put in place specific plans for responding, both publicly and privately, to the first high-profile incidents involving these systems. My sense, however, is that many organizations still have not created these “break-the-glass” or “break-glass” plans.

4) Earlier this semester, my impressive Law of the Newly Possible students did develop two thoughtful break-glass plans: one for the developers of automated driving systems and another for the regulators of these systems. Interestingly, although the private-sector group and the public-sector group each recognized the need to communicate with each other in the event of a crash, each also hesitated in reaching out to the other in the course of planning. In the real world, a broad range of stakeholders should be coordinating these plans sooner rather than later.

5) My book chapter on Regulation and the Risk of Inaction, also released this week, identifies eight public-sector strategies for managing risks related to automated driving. It can be freely downloaded here. A key point is that we must expect more of conventional drivers as well as automated vehicles. To paraphrase myself: I’m concerned about computer drivers, but I’m terrified about human drivers.

6) As always, please visit newlypossible.org for additional materials.

Tesla Motors autopilot (photo:Tesla)

An interesting article in last week’s Wall Street Journal spawned a series of unfortunate headlines (in a variety of publications) suggesting that Tesla had somehow “solved” the “problem” of “liability” by requiring that human drivers manually instruct the company’s autopilot to complete otherwise-automated lane changes.

(I have not asked Tesla what specifically it plans for its autopilot or what technical and legal analyses underlie its design decisions. The initial report may not and should not be the full story.)

For many reasons, these are silly headlines.

First, “liability” is a word that nonlawyers often use in clumsy reference to either (a) the existence of vague legal or policy questions related to the increasing automation of driving or (b) the specific but unhelpful question of “who is liable when an automated vehicle crashes.”

If this term is actually meant to refer, however imperfectly, to “driver compliance with the rules of the road,” then it is not clear to what problem or for what reason this lane-change requirement is a “solution.” For a foundational analysis of legality, including my recommendation that both developers and regulators carefully examine applicable vehicle codes, please see Automated Vehicles Are Probably Legal in the United States.

This term probably refers instead to some concept of fault following a crash. As a forthcoming paper explains, liability (or the broader notion of “responsibility”) is multifaceted:

Moreover, although liability is frequently posited as an either/or proposition (“either the manufacturer or the driver”), it is rarely binary. In terms of just civil (that is, noncriminal) liability, in a single incident a vehicle owner could be liable for failing to properly maintain her vehicle, a driver could be liable for improperly using the vehicle’s automation features, a manufacturer could be liable for failing to adequately warn the user, a sensor supplier could be liable for poorly manufacturing a spotty laser scanner, and a mapping data provider could be liable for providing incorrect roadway data. Or not.

The relative liability of various actors will depend on the particular facts of the particular incident. Although automation will certainly impact civil liability in both theory and practice, merely asking “who is liable in tomorrow’s automated crashes” in the abstract is like asking “who is liable in today’s conventional crashes.” The answer is: “It depends.”

Second, the only way to truly “solve” civil liability is to prevent the underlying injuries from occurring. If automated features actually improve traffic safety, they will help in this regard. As a technical matter, however, it is doubtful that requiring drivers merely to initiate lane changes will ensure that they are engaged enough in driving that they are able to quickly respond to the variety of bizarre situations that routinely occur on our roads. This is one of the difficult human-factors issues present in what I call the “mushy middle” of automation, principally SAE levels 2 and 3.

These levels of automation help ground discussions about the respective roles of the human driver and the automated driving system.

At SAE level 2, the human driver is still expected, as a technical matter, to monitor the driving environment and to immediately respond as needed. If this is Tesla’s expectation as well, then the lane-change requirement alone is manifestly inadequate: Many drivers will almost certainly consciously or unconsciously disengage from actively driving for long stretches without lane changes.

At SAE level 3, the human driver is no longer expected, as a technical matter, to monitor the driving environment but is expected to resume actively driving within an appropriate time after the automated system requests that she do so. This level raises difficult questions about ensuring and managing this human-machine transition. But it is technically daunting in another way as well: The vehicle must be able to drive itself for the several seconds it may take the human driver to effectively reengage. If this is ultimately Tesla’s claim, then it represents a significant technological breakthrough—and one reasonably subject to requests for substantiation.

Third, these two levels of automation raise specific liability issues that are not necessarily clarified by a lane-change requirement.

At SAE level 2, driver distraction is entirely foreseeable, and in some states Tesla might be held civilly liable for facilitating this distraction, for inadequately warning against it, or for inadequately designing against it. Victims of a crash involving a distracted Tesla driver would surely point to more robust (though hardly infallible) ways of monitoring driver attention—like requiring occasional contact with the steering wheel or monitoring eye movements—as proof that arguably safer designs are readily available.

At SAE level 3, Tesla would be asserting that its autopilot system could handle the demands of driving—from the routine to the rare—without a human driver monitoring the roadway or intervening immediately when needed. If this is the case, then Tesla may nonetheless face significant liability, particularly in the event of crashes that could have been prevented either by human drivers or by better automated systems.

Fourth, this potential liability does not preclude the development or deployment of these technologies—for reasons that I discuss here and here and here.

In summary, merely requiring drivers to initiate lane changes raises both technical and legal questions.

However, the idea that there can be technological fixes to legal quandaries is a powerful one that is explored in Part III of Proximity-Driven Liability.

A related example illustrates this idea. In the United States, automakers are generally required to design their vehicles to be reasonably safe for unbelted as well as belted occupants. (In contrast, European regulators assume that anyone who cares about their safety will wear their seatbelt.) In addition, some US states restrict whether or for what purpose a defendant automaker can introduce evidence that an injured plaintiff was not wearing her seatbelt.

An understandable legal response would be to update these laws for the 21st Century. Regardless, some developers of automated systems seem poised to adopt a technological response: Their automated systems will simply refuse to operate unless all of the vehicle’s occupants are wearing their seatbelts. Given that seatbelt use can cut the chance of severe injury or death by about half, this is a promising approach that must not be allowed to suffer the same fate as the interlocks of 40 years ago.

More broadly, any automaker must consider the complex interactions among design, law, use, and safety. As I argue in a forthcoming paper by the same name, Lawyers and Engineers Should Speak the Same Robot Language.

Google has done over 2.7 million km of testing with their existing fleet, they announced. Now, they will be putting their small “buggy” vehicle onto real streets in Mountain View. The cars will stick to slower streets and are NEVs that only go 25mph.

While this vehicle is designed for fully automatic operation, as required during the testing phase it will have a temporary set of controls for the safety driver to use in case of any problem. Google’s buggy, which still has no official name, has been built in a small fleet and has been operating on test tracks up to this point. Now it will need to operate among other road users and pedestrians.

Accidents with — but not caused by — self-driving cars

The press were terribly excited when reports filed with the State of California indicated that there had been four accidents reported — three for Google and one for Delphi. Google reported a total of eleven accidents in six years of testing and over 1.5 million miles.

Headlines spoke loudly about the cars being in accidents, but buried in the copy was the fact that none of the accidents by any company were the fault of the software. Several took place during human driving, and the rest were accidents that were clearly the fault of the other party, such as being rear ended or hit while stopped.

Still, some of press noticed that this accident rate is higher than normal, in fact almost double. Human drivers are in an accident about every 250,000 miles; at that rate Google should have had only six accidents during its 1.5 million miles.

The answer may be that these vehicles are unusual and have “self driving car” written on them. They may be distracting other drivers, making it more likely that those drivers will make a mistake. Anecdotally, people who have encountered a Google car on the road have told me “I thought about going in front of it and braking to see what it would do,”— though they said they didn’t do it in the end. Doing so would be both risky and obnoxious, and fortunately experience shows that it’s very rare for other drivers to actually try to “test” the car. But perhaps people who think about doing so distract themselves and end up in an accident — not good, of course, but it’s a problem that should diminish as the novelty of the cars decreases.

Mercedes and Freightliner test in Nevada

There was also lots of press about a combined project of Mercedes/Daimler and Freightliner to test a self-driving truck in Nevada. There is no reason that we won’t eventually have self-driving trucks, of course, and there are direct economic benefits for trucking fleets to not require drivers.

Self-driving trucks are not new off-road. In fact the first commercial self-driving vehicles were mining trucks at the Rio Tinto mine in Australia. Small startup Peleton is producing a system to let truckers convoy, with the rear driver able to go hands-free. Putting them on regular roads is a big step, but it opens some difficult questions.

First, it is not wise to do this early on. Systems will not be perfect, and there will be accidents. You want your first accidents to be with something like Google’s buggy or a Prius, not with an 18-wheel semi-truck. “Your first is your worst” with software and so your first should be small and light.

Secondly, this truck opens up the jobs question much more than other vehicles, where the main goal is to replace amateur drivers, not professionals. Yes, cab drivers will slowly fade out of existence as the decades pass, but nobody grows up wanting to be a cab driver — it’s a job you fall into for a short time because it’s quick and easy work that doesn’t need much training. While other people build robots to replace workers, the developers of self-driving cars are mostly working on saving lives and increasing convenience.

Many jobs have been changed by automation, of course, and this will keep happening, and it will happen faster. Truck drivers are just one group that will face this, and they are not the first. On the other hand, the reality is that while automation has happened at a grand scale, there are more people working today than ever. People move to other jobs, and they will continue to do so. This may not be much satisfaction for those who will need to go through this task, but the other benefits of robocars are so large that it’s hard to imagine delaying them because of this. Jobs are important, but lives are even more important.

It’s also worth noting that today there is a large shortage of truck drivers, and as such the early robotic trucks will not be taking any jobs.

I’m more interested in tiny delivery “trucks”, which I call “deliverbots.” For long haul, having large shared cargo vehicles makes sense, but for delivery, it can be better to have a small robot do the job and make it direct and personal.

New Sensors

The world of sensors continues to grow. This wideband software-based radar from a student team won a prize. It claims to produce a 3D image. Today’s automotive radars have long range but very low resolution. High resolution radar could replace radar if it gets enough resolution. Radar sees further, and sees through fog, and gives you a speed value, and LIDAR falls short in those areas.

Also noteworthy is this article on getting centimeter GPS accuracy with COTS GPS equipment. They claim to be able to eliminate a lot of multipath through random movements of the antennas. If true, it could be a huge localization breakthrough. GPS just isn’t good enough for robocar positioning. Aside from the fact it goes away in some locations like tunnels, and even though modern techniques can get sub-cm accuracy, if you want to position your robocar with it, and it alone, you need it to essentially never fail. But it does.

That said, most other localization systems, including map and image based localization, benefit from getting good GPS data to keep them reliable. The two systems together work very well, and making either one better helps.

Transportation Secretary Fox advances DoT plan

Secretary Fox has been out writing articles and Speaking in Silicon Valley about their Beyond Traffic effort. They promise big promotion of robocars, which is good. Sadly, they also keep promoting the false idea that vehicle to vehicle communications are valuable and will play a significant role in the development of robocars. In my view, many inside the DoT staked their careers on V2V, and so feel required to promote it even though it has minimal compelling applications and may actually be rejected entirely by the robocar community because of security issues.

This debate is going to continue for a while, it seems.

Maps, maps, maps

Nokia has put its “Here” map division up for sale, and a large part of the attention seems to related to their HD Maps project, aimed at making maps for self-driving. (HERE published a short interview with me on the value of these maps.)

It will be interesting to see how much money that commands. At the same time, TomTom, another mapping company, has announced it will begin making maps for self-driving cars — a decision they made in part because of encouragement from yours truly.

Uber dwarfs taxis

Many who thought Uber’s valuation is crazy came to that conclusion because they looked at the size of the Taxi industry. To the surprise of nobody who has followed Uber, they recently revealed that in San Francisco, their birthplace, they are now three times the size of the old taxi industry, and growing. It was entirely the wrong comparison to make. The same is true of robocars. They won’t just match what Uber does, they will change the world.

There’s more news to come during a brief visit home, but I’m off to play in Peoria, and then Africa next week!

This article originally appeared on robocars.com.

• Issues in regulating robocars, and the case for a light hand
• Robots Podcast: Sensors for autonomous driving
• Automated Driving Fail
• Programming safety into self-driving cars

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

To my surprise, there was a huge rush of press coverage of the study, which concluded that 6 to 12% of car passengers get a bit queasy, especially when looking down in order to read or work. While it was worthwhile to work up those numbers, the overall revelation was in the “Duh” category for me, I guess because it happens to me on some roads and I presumed it was fairly common.

Oddly, most of the press was of the “this is going to be a barrier to self-driving cars” sort, while my reaction was, “wow, that happens to fewer people than I thought!”

Having always known this, I am interested in the statistics, but to me the much more interesting question is, “what can be done about it?”

For those who don’t like to face backwards, the fact that so many are not bothered is a good sign — just switch seats.

Some activities are clearly better than others. While staring down at your phone or computer in your lap is bad during turns and bumps, it may be that staring up at a screen watching a video, with your peripheral vision very connected to the environment, is a choice that reduces the stress.

I also am interested in studying if there can be clues to help people reduce sickness. For example, the car will know of upcoming turns, and probably even upcoming bumps. It could issue tones to give you subtle clues as to what’s coming, and when it might be time to pause and look up. It might even be the case that audio clues could substitute for visual clues in our plastic brains.

Another interesting thing to test would be having your tablet or phone deliberately tilt its display to give you the illusion you are looking at the fixed world when you look at it, or to have a little “window” that shows you a real world level so your eyes and inner ears can find something to agree on.

More advanced would be a passenger pod on hydraulic struts able to tilt with several degrees of freedom to counter the turns and bumps, and make them always be such that the forces go up and down, never side to side. With proper banking and tilting, you could go through a roundabout (often quite disconcerting when staring down) but only feel yourself get lighter and heavier.

This post originally appeared on Robocars.com. If you liked this article, you may also be interested in:

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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

In the pre-history of robocars, it was common to say that it would be ages before they were legal. Then, at least in some countries, groups like Google discovered they were already legal by default, at least for testing. When the vehicle codes were written long ago, nobody thought to say “no robots.”

A few places have laws that require that certain driving activities be done by humans, but many don’t. Even so, Google and a few others sought to make the regulatory status of the cars more clear. Soon the state of Nevada passed a bill requiring their DMV to write regulations. Several other states passed similar laws. The first regulations mostly defined testing, and some are leading to covering operation — first under human supervision, eventually without a person in the car.

At the same time, the National Highway Transportation Safety Agency (NHTSA,) which writes the safety standards for car manufacturers — the rules that demand seatbelts, airbags and electronic stability control — were encouraged to see what their role should be. They published a document defining a taxonomy of vehicles and suggestions on how to regulate them.

The first reaction of many is, “of course.” It’s common to think that it would be strange to put these vehicles on the road without the government certifying them and laying out standards for how they should work. Contrary to that intuition, that is not the way government regulation of automotive technologies has worked, nor the way it should work. And that turns out to be even more true for robocars than the technologies that came before them.

To address some of these issues, I have prepared a new special article on the issues around regulating robocars. The article concludes that in spite of a frequent claim that we want to regulate and standarize even before the technology has been out in the market for a while, this is in fact both a highly unusual approach, and possibly even a dangerous approach. In summary:

• Regulations are normally developed long after technologies are in the market, not before they are developed. That’s true for almost all automotive safety technologies, like seat belts, airbags, anti-lock brakes, crumple zones, and more.
• When regulations do come, their usual purpose is not to govern how to build the safety technologies, but to demand that the car makers that are not including them start including them.
• Many of the driving-related technologies — adaptive cruise control, lane departure warning and lane-keeping, automatic parking, forward collision warning and avoidance and blindspot warning — all remain unregulated today.
• Self-certification by the vendor is the norm, not the exception. While some things (like crash tests) are done by the government or insurance labs, most testing and certification is done by vendors.
• Tort law already provides a strong legal force for safety, and in fact it may already be too strong rather than not strong enough.
• Even the builders of robocars have not yet figured out precisely how they will attain and test their safety goals, so we are at least a decade out from regulators figuring that out. The technology is changing so fast they may never catch up, requiring other solutions.
• We need to protect the ability of small players and tinkerers to innovate, even with dangerous technology like cars — just as we always have.

The full article is available on robocars.com: Regulating Robocar Safety: An examination of the issues around regulating robocar safety and the case for a very light touch.

In this episode, Audrow Nash interviews Christoph Stiller from the Karlsruhe Institute of Technology. Stiller speaks about the sensors required for various level of autonomous driving, as well as the ethics of autonomous cars, and his experience in the Defense Advanced Research Projects Agency (DARPA) Grand Challenge.

Christoph Stiller

Christoph Stiller studied Electrical Engineering in Aachen, Germany and Trondheim, Norway, and received the Diploma degree and the Dr.-Ing. degree (Ph.D.) from Aachen University of Technology in 1988 and 1994, respectively. He worked with INRS-Telecommunications in Montreal, Canada for a post-doctoral year as Member of the Scientific Staff in 1994/1995. In 1995 he joined the Corporate Research and Advanced Development of Robert Bosch GmbH, Germany. In 2001 he became chaired professor and director of the Institute for Measurement and Control Systems at Karlsruhe Institute of Technology, Germany.

Dr. Stiller serves as immediate Past President of the IEEE Intelligent Transportation Systems Society, Associate Editor for the IEEE Transactions on Intelligent Transportation Systems (2004-ongoing), IEEE Transactions on Image Processing (1999-2003) and for the IEEE Intelligent Transportation Systems Magazine (2012-ongoing). He served as Editor-in-Chief of the IEEE Intelligent Transportation Systems Magazine (2009-2011). He has been program chair of the IEEE Intelligent Vehicles Symposium 2004 in Italy and General Chair of the IEEE Intelligent Vehicles Symposium 2011 in Germany. His automated driving team AnnieWAY has been finalist in the Darpa Urban Challenge 2007 and winner of the Grand Cooperative Driving Challenge in 2011.

Links:

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Transcript

Audrow Nash:  Hi, welcome to Robots Podcast. Can you introduce yourself?

Christoph Stiller:  My name’s Christoph Stiller. I’m a professor in Mechanical Engineering at Karlsruhe Institute of Technology in Germany.

Audrow Nash:  Can you tell me the goal and motivation behind your research?

Christoph Stiller:  We’re working on autonomous cars, and in particular we focus on computer vision for vehicles (cars that see their environment), and on trajectory planning for vehicles (the decision on where to drive, given the world that the car sees).

Audrow Nash:  What kind of sensors do you use on your vehicles?

Christoph Stiller:  Our main sensors are video cameras, but we also do experiments with LIDAR and radar sensors.

Audrow Nash:  You have a car named Bertha, and it does not have roof-mounted sensors. Can you tell me a bit about that?

Christoph Stiller:  Bertha was developed together with Mercedes Benz and the goal was to use only close-to-market sensors. In particular, we only used cameras that were looking around the vehicle, and radar sensors that came from serious production levels. No expensive GPS, no expensive LIDAR sensors, and in particular, nothing on the roof.

Audrow Nash:  What is the motivation behind not using roof sensors?

Christoph Stiller:  Our other vehicle was driving with roof sensors and those sensors were quite expensive; we had LIDAR sensors that cost us about $70,000 each, and we had a high precision GPS and with an inertial measurement unit and that cost us about the same amount of money on top. That’s far from serious production.

Audrow Nash:  Are there other disadvantages to using roof sensors? For example they get dirty, was that a concern?

Christoph Stiller:  Yes, of course. If a roof sensor is not cleaned it will get dirty, just like how the windshield on your car gets so dirty that you can’t look out anymore after a few hundred kilometers of driving. The same happens to a camera lens: if you drive with your camera lens for a long time, or with LIDAR sensors (which are also optical systems), the lens of that system gets contaminated with dirt or insects, and so the sensor gets blind. In the best case you notice that you can’t drive autonomously anymore.

Audrow Nash:  If your sensors are not on the roof, where are they in the car?

Christoph Stiller:  [Human drivers look out the window. In our system] they’re very close to the roof behind the windshield. When the driver gets irritated by a dirty window, and he engages the wipers, and then our cameras are also cleaned because it’s in the wiper area.

Audrow Nash:  What other sensors do you have in addition to the vision system that watches what’s happening ahead of the car?

Christoph Stiller:  We also have radar sensors. Many cars already have one radar sensor, but we equipped our vehicle with three radar sensors. These came from serious production because we needed the larger viewing angle for those sensors so that we could look into the traffic area and see whether [there was] an oncoming vehicle that we would need to consider.

We also use a standard GPS unit, but it has a precision of about 20 meters, similar to what you have in your smartphone. It’s not an expensive unit, it’s only a low precision unit that gives us a very coarse positioning. We prerecord that tell us where to drive, which lanes we’re supposed to take, what possibilities we have, who has precedence at what intersection, where traffic lights are … all this information is stored in maps.

Audrow Nash:  You mentioned in your abstract that these sensors, the ones you’re using now on Bertha, are close to market. What do you mean by this?

Christoph Stiller:  [The radar sensors are in the market, but not in high numbers.] The video cameras are in the market already, but ours of course require particular algorithms to analyze the data and that’s done on a personal computer in the trunk and of course we would need to bring that down to some embedded system hardware for the processing. That’s something that could be done in a reasonable amount of time.

Audrow Nash:  What are some of the major barriers to bringing these sensors to market?

Christoph Stiller:  The sensors could be brought to market, but the main barriers to bring the whole system to market would be that right now we need a safety driver who could intervene in case of very rare situations that were not predicted. For example if you have an emergency vehicle coming on to you or some other situation that is very difficult to detect, you need human intelligence to keep the vehicle in a safe state.

Audrow Nash:  Can you tell me a bit about the real-time decision-making that your systems do?

Christoph Stiller:  The vehicle first analyses all the sensor signals. It looks first of all where it is on the map with high precision; it uses GPS for a coarse 20-meter precise position, and then it does visual localization, looking for landmarks in the environment such as a window of a house or a tree. Then, like humans do, the system is able to localize itself, except that our visual localization system is very precise: we get a precision of about five-centimeter accuracy, so we know with high accuracy where we are. Then we look at the map to see what lane we are in, the lane boundaries and any obstacle that is in our driving corridor (such as other cars, pedestrians, bicyclists, parked cars or any other static obstacles), and then we plan a path that does not collide with any static or moving obstacles if possible.

We first try to stay in our lane, but if that’s not possible we have a decision unit that allows us to move to another lane. This might be an adjacent lane that moves in the same direction if there’s a gap, and if that’s not possible either, we could even decide to go into the [opposing] lane if there is no vehicle coming in that direction. If none of this is possible, of course, we’ll stay and wait until the situation resolves.

Audrow Nash:  In the real world, the maps that you correlate all of your sensor data with are sometimes unreliable. Can you talk about those challenges?

Christoph Stiller:  Right now if our map is wrong, for example a new construction zone is built in the area, our supervising driver has to react. We have a safety driver onboard who doesn’t do anything, except in an unexpected situation like when the map is wrong, or when we have an emergency vehicle. Of course [in the marketplace] you can’t have a safety driver delivered with every car, and so the long-term goal is then to keep the map up to date, and that will be done with community mapping. There will be a whole crowd of cars that are equipped with sensors that communicate slight changes in the map to an infrastructure server, and the infrastructure server would accumulate that information. If many vehicles [report a change to the map], let’s say lane has moved slightly due to a construction, then the map will signal that to the vehicles that follow.

Each vehicle has an accurate map and an up-to-date map of the environment.

Audrow Nash:  So it’s essentially crowdsourcing the development of the map for all the cars to pull from?

Christoph Stiller:  Yes, it’s a crowdsourcing method.

Audrow Nash:  What was your experience with the grand cooperative driving challenges, the series of them?

Christoph Stiller:  There’s a large series that was started in the US by DARPA in 2004 and 2005. These were grand challenges where the goal was to drive through the desert autonomously. Then in 2007 there was an urban challenge that was through an area with low houses, almost no trees, very few traffic lights, very few traffic signs, and no bicycles. So it was not a fully normal traffic situation but more a mock-up of that.

In 2011 there was the first cooperative driving challenge, which took place in Holland. The goal there was to drive in a tight platoon of vehicles, keeping a very short distance – 6 meters – [between the cars]. If you are driving 100km/h that is about 34 meters per second, and that means it’s 0.2 seconds [between you and] your predecessor. Of course it’s impossible for a human driver to react in such a short time, so these vehicles could only be driven autonomously.

The first vehicle would [break hard], and all the other vehicles then of course had to react in real-time to avoid collision. The vehicles needed to communicate – that’s why it was called cooperative challenge. They had to communicate their position and velocity, and if they wanted to, they could also communicate their acceleration, which of course is highly beneficial to following vehicles.

Audrow Nash:  What were some of the major challenges you encountered in the DARPA cooperative driving challenge?

Christoph Stiller:  The major challenges were the short reaction time, and the fact that the teams were heterogeneous. It was not a system approach, developing one control law for all the vehicles, which would then have a guaranteed stability for the whole platoon. We didn’t know the control laws of the other [vehicles]. The challenge was to have a very robust system that could drive safe without knowing what strategy other [vehicles] have.

Audrow Nash:  What did you learn from this?

Christoph Stiller:  We learned that it was possible in such scenarios to damp very severe oscillations, allowing the first car to [break almost to a full stop] and have all the vehicles in the platoon (from small cars to trucks) avoid a collision through timely braking and communication. We learned that communication could help [prevent] what we call shockwaves in traffic. [This can happen] when one car brakes in a platoon, and each following car has to brake stronger than its predecessor and eventually either one car can’t break as strong as it should, so there’s a collision, or it comes to full stop and then you have traffic congestion.

Audrow Nash:  I’m interested in your perspective on the ethics of driving autonomous vehicles. What would you think about the delegation of responsibility between the driver and the manufacturers of the autonomous car sensors?

Christoph Stiller:  That will shift. Right now for most accidents, the car driver is responsible, so most accidents in traffic are due to human fault. A very small number of traffic accidents come from the responsibility of construction fault by the car manufacturer. Of course, the more autonomous maneuvers introduced into the market, the greater the likelihood that one of those maneuvers is ill-designed and could cause a crash. In that case of course the car manufacturer would be liable. If in the long term the vehicle is fully autonomous, so that [a person] could read a newspaper, then of course the car manufacturer is responsible for all the traffic accidents that happens while he’s driving autonomously.

Audrow Nash:  So, while the user is driving, they’re responsible if the car gets in an accident, but when when the autonomous car takes over, then it’s me manufacturer’s liability?

Christoph Stiller:  Yes, that’s my understanding of liability. That you’re only liable for what you really do. If the car manufacturer automates a car, then of course the driver to my understanding cannot be responsible. There maybe a situation, with highly automated cars where the driver is allowed to read a newspaper but before some situation comes that cannot be handled by the car like a construction site, the vehicle tells the driver, “You have to take over in 10 seconds,”  … then of course for the portion where the vehicle drives autonomously, the car manufacturer is responsible, and for the [portion] that the driver drives manually, the driver is responsible for his driving. And in between, at the times when the vehicle says you have to take over, this is a responsibility of the driver to be ready to take over; he can’t sleep in the rear seat or be drunk. He has to be ready to take over in such a situation.

Audrow Nash:  What kind of timeline do you believe we will have for autonomous cars?

Christoph Stiller:  This is a very difficult question. We do have some autonomous functions in the market already. Most [high end] cars already do automated emergency braking before an imminent collision, and for pedestrians they even do automated evasive steering. Those functions will merge. Today there are very rare situations where the vehicle is certain that it can handle the situation better than the driver; the number of these situations will grow, the vehicle will get into more and more situations and understand them better than the driver, and if it understands that the situation is critical, at least then it will take over and avoid a collision, or at least minimize the velocity of the collision and therefore the severity of the collision. Eventually all those functions will come to the point where the car can drive autonomously everywhere and any time and the driver doesn’t even need to be in the car, but that will take a long time.

Audrow Nash:  I know there are many major challenges before autonomous cars are integrated into society. What do you believe is one of the big ones?

Christoph Stiller:  In my experience, the most difficult thing is to understand the situation correctly. An example would be to consider a pedestrian walking to the curb and stopping for a moment. A human driver can predict, with very high precision, whether or not the pedestrian will walk on the road or will stop and wait for the vehicle to pass. You look at whether a pedestrian looks towards you, you consider their age, and you consider whether the person is wearing earphones and not paying attention. Taken together, all those small factors together tell you whether you should break, blow your horn, or just drive through. To do the same would be very difficult with the current state-of-the-art autonomous vehicle.

Audrow Nash:  Wrapping up, what do you think is the future of robotics?

Christoph Stiller:  As far as cars are concerned, I’m certain that autonomy will come, and in parallel, we will see cooperation, with vehicles communicating with each other and using that to harmonize their trajectories. Cars will follow each other at a closer distance and have maneuvers that are communicated with each other, so they will know exactly what the others are doing. That would harmonize traffic flow, and of course improve safety. In the very long term, I expect that traffic would then be harmonized so much that the flow of traffic would look like a fish swarm movement rather than the chaotic movement that we have on the roads today.

*Transcripts are edited for clarity with great care. However, we cannot assume any responsibility for their accuracy.

The driving routes (in green) computed by a Lexicographic Value Iteration (LVI) algorithm for an attentive driver (above) and a tired driver (below) based on traffic and road conditions. Credit: Shlomo Zilberstein, University of Massachusetts Amherst

For decades, researchers in artificial intelligence, or AI, worked on specialized problems, developing theoretical concepts and workable algorithms for various aspects of the field. Computer vision, planning and reasoning experts all struggled independently in areas that many thought would be easy to solve, but which proved incredibly difficult.

However, in recent years, as the individual aspects of artificial intelligence matured, researchers began bringing the pieces together, leading to amazing displays of high-level intelligence: from IBM’s Watson to the recent poker playing champion to the ability of AI to recognize cats on the internet.

These advances were on display last week at the 29th conference of the Association for the Advancement of Artificial Intelligence (AAAI) in Austin, Texas, where interdisciplinary and applied research were prevalent, according to Shlomo Zilberstein, the conference committee chair and co-author on three papers at the conference.

Zilberstein studies the way artificial agents plan their future actions, particularly when working semi-autonomously — that is to say in conjunction with people or other devices.

Examples of semi-autonomous systems include co-robots working with humans in manufacturing, search-and-rescue robots that can be managed by humans working  remotely and “driverless” cars. It is the latter topic that has particularly piqued Zilberstein’s interest in recent years.

The marketing campaigns of leading auto manufacturers have presented a vision of the future where the passenger (formerly known as the driver) can check his or her email, chat with friends or even sleep while shuttling between home and the office. Some prototype vehicles included seats that swivel back to create an interior living room, or as in the case of Google’s driverless car, a design with no steering wheel or brakes.

Except in rare cases, it’s not clear to Zilberstein that this vision for the vehicles of the near future is a realistic one.

“In many areas, there are lots of barriers to full autonomy,” Zilberstein said. “These barriers are not only technological, but also relate to legal and ethical issues and economic concerns.”

In his talk at the “Blue Sky” session at AAAI, Zilberstein argued that in many areas, including driving, we will go through a long period where humans act as co-pilots or supervisors, passing off responsibility to the vehicle when possible and taking the wheel when the driving gets tricky, before the technology reaches full autonomy (if it ever does).

In such a scenario, the car would need to communicate with drivers to alert them when they need to take over control. In cases where the driver is non-responsive, the car must be able to autonomously make the decision to safely move to the side of the road and stop.

“People are unpredictable. What happens if the person is not doing what they’re asked or expected to do, and the car is moving at sixty miles per hour?” Zilberstein asked. “This requires ‘fault-tolerant planning.’ It’s the kind of planning that can handle a certain number of deviations or errors by the person who is asked to execute the plan.”

With support from the National Science Foundation (NSF), Zilberstein has been exploring these and other practical questions related to the possibility of artificial agents that act among us.

Zilberstein, a professor of computer science at the University of Massachusetts Amherst, works with human studies experts from academia and industry to help uncover the subtle elements of human behavior that one would need to take into account when preparing a robot to work semi-autonomously. He then translates those ideas into computer programs that let a robot or autonomous vehicle plan its actions — and create a plan B in case of an emergency.

There are a lot of subtle cues that go into safe driving. Take for example a four-way stop. Officially, the first car to the crosswalk goes first, but in actuality, people watch each other to see if and when to make their move.

“There is a slight negotiation going on without talking,” Zilberstein explained. “It’s communicating by your action such as eye contact, the wave of a hand, or the slight revving of an engine.”

In trials, autonomous vehicles often sit paralyzed at such stops, unable to safely read the cues of the other drivers on the road. This “undecidedness” is a big problem for robots. A recent paper by Alan Winfield of Bristol Robotics Laboratory in the UK showed how robots, when faced with a difficult decision, will often process for such a long period of time as to miss the opportunity to act. Zilberstein’s systems are designed to remedy this problem.

“With some careful separation of objectives, planning algorithms could address one of the key problems of maintaining ‘live state’, even when goal reachability relies on timely human interventions,” he concluded.

The ability to tailor one’s trip based on human-centered factors — like how attentive the driver can be or the driver’s desire to avoid highways — is another aspect of semi-autonomous driving that Zilberstein is exploring.

In a paper with Kyle Wray from the University of Massachusetts Amherst and Abdel-Illah Mouaddib from the University of Caen in France, Zilberstein introduced a new model and planning algorithm that allows semi-autonomous systems to make sequential decisions in situations that involve multiple objectives — for example, balancing safety and speed.

Their experiment focused on a semi-autonomous driving scenario where the decision to transfer control depended on the driver’s level of fatigue. They showed that using their new algorithm a vehicle was able to favor roads where the vehicle can drive autonomously when the driver is fatigued, thus maximizing driver safety.

“In real life, people often try to optimize several competing objectives,” Zilberstein said. “This planning algorithm can do that very quickly when the objectives are prioritized. For example, the highest priority may be to minimize driving time and a lower priority objective may be to minimize driving effort. Ultimately, we want to learn how to balance such competing objectives for each driver based on observed driving patterns.”

It’s an exciting time for artificial intelligence. The fruits of many decades of labor are finally being deployed in real systems and machine learning is being adopted widely and for different purposes than anyone had ever realized.

“We are beginning to see these kinds of remarkable successes that integrate decades-long research efforts in a variety of AI topics,” said Héctor Muñoz-Avila, program director in NSF’s Robust Intelligence cluster.

Indeed, over many decades, NSF’s Robust Intelligence program has supported foundational research in artificial intelligence that, according to Zilberstein, has given rise to the amazing smart systems that are beginning to transform our world. But the agency has also supported researchers like Zilberstein who ask tough questions about emerging technologies.

“When we talk about autonomy, there are legal issues, technological issues and a lot of open questions,” he said. “Personally, I think that NSF has been able to identify these as important questions and has been willing to put money into them. And this gives the U.S. a big advantage.”

Investigators
Shlomo Zilberstein
Donald L. Fisher
Claudia Goldman
Kyle Hollins Wray
Luis Pineda
Avinoam Borowsky
Richard G. Freedman
Abdel-Illah Mouaddib

Related Institutions/Organizations
University of Massachusetts Amherst

Years Research Conducted
1996 – 2018

Total Grants
$2,385,857

Related Websites
The Resource-Bounded Reasoning Lab at the University of Massachusetts, Amherst: http://rbr.cs.umass.edu/

Photo: General Motors

Let me confess a secret fear. I suspect that the first “autopilot” functions on cars are going to be a bit boring.

I’m talking the offerings like traffic jam assist from Mercedes, super cruise from Cadillac and others. The faster highway assist versions that combine ADAS functions like lane-keeping and adaptive cruise control to keep the car in its lane and a fixed distance from the car in front of you. What Tesla has promoted and what scrappy startup “Cruise” plans to offer as a retrofit later this year. This is, in NHTSA’s flawed “levels” document what could be called supervision type 2.

Some of them also offer lane change, if you approve the safety of the change.

All these products will drive your car, slow or fast on highways, but they require your supervision. They may fail to find the lane in certain circumstances, because the makers are badly painted, or confusing, or just missing, or the light is wrong. When they do they’ll kick out and insist you drive. They’ll really insist, and you are expected to be behind the wheel, watching and grabbing it quickly — ideally even noticing the failure before the system does.

Some will kick out quite rarely. Others will do it several times during a typical commute. But the makers will insist you be vigilant, not just to cover their butts legally, but because in many situations you really do need to be vigilant.

Source: Wikimedia Commons

Testing shows that operators of these cars get pretty confident, especially if they are not kicking out very often. They do things they are told not to do. Pick up things to read. Do e-mails and texts. This is no surprise — people are texting even now when the car isn’t driving for them at all.

To reduce that, most companies are planning what they call “countermeasures” to make sure you are paying attention to the road. Some of them make you touch the wheel every 8 to 10 seconds. Some will have a camera watching your eyes that sounds an alarm if you look away from the road for too long. If you don’t keep alert, and ignore the alarms, the cars will either come to a stop in the middle of the freeway, or perhaps even just steer wild and run off the road. Some vendors are talking about how to get the car to pull off safely to the side of the road.

There is debate about whether all this will work, whether the countermeasures or other techniques will assure safety. But let’s leave that aside for a moment, and assume it works, and people stay safe.

I’m now asking the harder question, is this a worthwhile product? I’ve touted it as a milestone — a first product put out to customers. That Mercedes offered traffic jam assist in the 2014 S-Class and others followed with that and freeway autopilots is something I tell people in my talks to make it clear this is not just science fiction ideas and cute prototypes. Real, commercial development is underway.

That’s all true, and I would like these products. What I fear though, is whether it will be that much more useful or as relaxing as adaptive cruise control (ACC.) You probably don’t have ACC in your car. Uptake on it is quite low — as an individual add-on, usually costing $1,000 to $2,000, only 1-2% of car buyers get it. It’s much more commonly purchased as part of a “technology package” for more money, and it’s not sure what the driving force behind the purchase is.

Highway and traffic jam autopilot is just a “pleasant” feature, as is ACC. It makes driving a bit more relaxing, once you trust it. But it doesn’t change the world, not at all.

I admit to not having this in my car yet. I’ve sat in the driver’s seat of Google’s car some number of times, but there I’ve been on duty to watch it carefully. I got special driver training to assure I had the skills to deal with problem situations. It’s very interesting, but not relaxing. Some folks who have commuted long term in such cars have reported it to
be relaxing.

A step to greater things?

If highway autopilot is just a luxury feature, and doesn’t change the world, is it a stepping stone to something that does? From a standpoint of marketing, and customer and public reaction, it is. From a technical standpoint, I am not so sure.

Highway cruise products are being created out of existing ADAS tools — like ACC, FCA (Forward Collision Avoidance) and Lane-keeping. Those tools are great, but they are almost entirely created from radars and camera systems (most commonly the MobilEye.)

You learn something from driving with those, and you push progress in machine vision, computer vision and radar, but I suspect that the real robocars will not be based on ADAS technologies, or even evolutions of ADAS technologies— not at first.

I am highly confident that standby and unsupervised cars will work with LIDAR as their primary sensor for localization and perception of other vehicles on the road. That’s because LIDAR is quickly becoming cheap, and cameras are not yet able to delivery the reliability levels desired. Nobody, no matter how much they love vision-based systems, is going to leave out LIDAR on the first vehicles just to save $500. The first car is the car of greatest risk, and you will do all you can reasonably do to make it as safe as you can.

This is the error of the NHTSA and SAE “levels.” These levels have a focus on the amount of human supervision a car needs to be safe. Level 2 is “Fully supervised.” Level 3 is “Standby” — a human is not supervising ordinarily but must be standing by to take control on modest (non-super-urgent) notice. Levels 4 and 5 are “unsupervised.”

NHTSA’s 3 categories of supervision do have one valid ordering, in that doing 4 is harder than 3, and 3 is harder than 2. This is misleading, though, as they are not an evolution plan. Standby doesn’t necessarily derive from the technologies used in fully supervised. Unsupervised will use some of the technologies of standby, but it also will invent some entirely different paths to the solution. SAE has even explicitly declared the numbers are not an evolution plan in their latest revision of their document.

Google has issued doubts about the Standby category. They built it and tested it with commuting employees, though told them to supervise just in case. They found the employees after a week or two of use quickly did things to make the team uncomfortable. The transition from standby mode to manual driving is a complex and risky one — one that Google decided to simply eliminate in its next generation of prototypes.

The first commercial robocar was the Navia, which is in the Unsupervised category over private roads on a campus. It has no wheel, it was never sold to operate in supervised or standby modes. In spite of the ordering of the levels, it was the “level” 4 that came first, ahead of 2 and 3. (Level 1 is just regular ADAS and has been here for years.)

The Supervised category of products being released now still have their value, beyond the luxury they may offer their users. Driving huge numbers of road miles teaches us much, particularly learning about all the times the systems fail. While you won’t build a standby or unsupervised system out of ADAS tools, the ADAS tools actually offer a great fallback approach if the superior systems fail. While it might be too risky to drive 100 miles on an ADAS derived system without supervision, driving 0.1 miles to pull off a road after failure of another system may be a reasonable safety decision.

By coming out first, these systems will actually get the most “practice,” which is to say the most experience. The more failures, the more work that can be done to fix them, and the better ability to know what you can handle safely and what you can’t.

Radar is also likely to be a tool of all systems — it’s just too useful, too superhuman in its abilities to not make use of it. The supervised systems are teaching us more lessons about how to use and not use radar. Particularly worthwhile are newer efforts to make radar work on stopped objects. Radar sees through the most dense fog, which nothing else does.

A version of this article originally appeared on Robocars.com.

The taxi doesn’t park

One of the simplest solutions to parking involves robotaxi service. Such vehicles don’t really park, at least not where they dropped you off. They drop you off and go to their next customer. If they don’t have another ride, they can deliberately go to a place where they know they can easily park to wait. They don’t need to tackle a parking space that’s challenging at all.

Simple non-crowded lots

Parking in basic parking lots — typical open ground lots that are not close to full — is a pretty easy problem. So easy in fact, that we’ve seen a number of demonstrations, ranging back to Junior 3 and Audi Piloted Parking. Cars in the showroom now will identify parking spots for you (and tell you if you fit.) They have done basic parallel parking (with you on the brakes) for several years, and are starting to now even do it with you out of the car (but watching from a distance.) At CES, VW showed the special case of parking in your own garage or driveway, where you show the car where it’s going to go.

The early demos required empty parking lots with no pedestrians, and even no other moving cars, but today reasonably well-behaved other cars should not be a big problem. That’s the thing about non-crowded lots: people are not hunting or competing for spaces. The robocars would actually be very happy to seek out the large empty sections at the back of most parking lots because you aren’t going to be walking out that far; the car is going to come get you.

The biggest issue is the question of pedestrians who can appear out from behind a minivan. The answer to this is simply that vehicles that are parking can and do go slow, and slow automatically gives you a big safety boost. At parking lot speed, you really can stop very quickly if a pedestrian appears out of nowhere.

Junior 3 entering a parking lot, hunting for a space and taking it — in 2009.

More crowded lots

The challenge of parking lots, in spite of the low speeds, is that they don’t have well defined rules of the road. People ignore the arrows on the ground. They pause and wait for cars to exit. In really crowded lots, cars follow people who are leaving at walking speed, hoping to get dibs on their spot. They wait, blocking traffic, for a spot they claim as theirs. People fight for spots and steal spots. People park badly and cross over the lines.

As far as I know, nobody has tried to solve this challenge, and so it remains unsolved. It is one of the few problems in robocars that actually deserves the label of “AI,” though some think all driving is AI.

Even so, on the grand scheme of things, my intuition is that this is not one of the grand unsolved challenges of AI. Parking lots don’t have legalized rules of the road, but they do have rules and principles, and we all learn them the more we park. Creating a system that can do well with these rules using various AI tools seems like a doable challenge when the time comes. This system will be able to take advantage of a couple of special abilities of the robocars:

• They will be able to park and exit spots quickly and efficiently. They won’t be like the people you always see who do a 5 point turn to exit their parking spot when you (but not they) can see they still have 5 feet of room behind them.
• In general, they will be superb parkers, centering themselves as well as possible inside spots
• They don’t need room to open their doors, so they can park right next to walls and pillars.
• Yes, they could also park right next to badly parked cars that have encroached into other spaces and thus made a space no human can use. There is a risk of course that the bad parker, who finds they can’t get in one side, might retaliate. (I’ve had a guy rip my mirror off in revenge.) In this case, though, they will have a photo of the licence plate and a sensor record of the revenge taking place!
• In the event of problems or deadlock, they are open to the idea of just giving up and parking somewhere farther away that is easier to park in. Unlike humans they could drive as quickly in reverse as forward to back out of situations.

In spite of all this, the cars will want to avoid the full parking lots where the chaos happens. If there is another lot not far away, they will just go there, and require a couple minutes more advance notice from their master when summoned to pick them up. If there is nowhere nearby to park, the car will tell its passenger that she has to do the parking.

Robo-valet zones

Even in the most crowded lots, there is the potential to easily create zones of the parking lot that are marked:

“Robot Valet Parking only. All other cars may be blocked in or towed. No pedestrians.”

In the car’s map, it will indicate what server is handling the robo-valet section, though it is possible to have it work without any communication at all.

In the most basic version the car would ask permission to enter the lot. The database might even assign it a spot, but generally it would just enter and take any spot. By “any spot”, I mean any piece of pavement, ignoring the lines on the ground. It would park tightly packed next to the other cars. It would report where it parked to the database, as well as how to send it a message, and when it expects to leave.

Other cars would arrive. Eventually one would block in your car. If the database has given them a way to communicate (probably over the internet, though if they had V2V they could use that) they might discuss who plans to leave first, and the cars would adjust themselves to put the cars that will leave sooner at the front. This is strongly in the interests of the cars. If you plan to be there a while, you want to go to the back so you don’t have to keep moving to let cars behind you out. But it still works, just not as well, if the cars just take any available spot.

When it’s time to leave, the cars could try to send a message over the data networks to the cars in front of them, but a simpler approach might be to just nudge slightly forward — a few cm will do it. This will cause the car in the direction of the nudge to notice, and it too would nudge forward, and so on, and so on until the front car moves out, and then all the cars in that row can move out, including your car, which leaves the lot. Then the other cars can move in to fill the spot. If they have a database that maps the cars in that section, they could try to be clever in how they re-fill the empty column to minimize movement.

There are even faster algorithms if you leave a few empty spaces. Robocars have the ability to move in concert to “move the space” and put it next to a car that wants to exit. It’s more efficient, but not needed.

The database becomes more useful if a human driver ignores the signs and tries to park in the lot. That’s because the database is the simplest way of spotting a vehicle that’s not supposed to be there. As a first step, the cars in the lot could start flashing their lights and honking their horns at the interloper, or even speak human language messages out a speaker. “Hey, this is the robot valet lot, you are blocking me in! We’re calling a tow truck to come remove you if you don’t leave.” Some idiots may still try, and the robots could arrange so that almost all of them can still get out, and if not, they might call that tow truck.

The robo-valet section can be at the back of the parking lot, or the top of a structure — those places the humans park in last. The owner of the lot has a huge incentive to do this, since they can make much more efficient use of their land with the tight valet-dense parking. All the owner has to do is register the lot section in a database — a database that a company like Google would probably be happy to offer for free to benefit their cars.

Human valets could also park cars in this area. They would just need to use an app on their smartphone that tells them where to park and allows them to register that they did it. The robots will want the human-parked cars to park at the back, because they will move out of the way when it’s time for the human parked car to be driven back out.

The main requirements for this parking area would be that it be reachable from the outside without going through a zone of chaos, and that it then be possible to also reach the pickup/dropoff point for passengers without the risk of getting stuck in chaos. Larger lots tend to have entrance lanes without spots on them that serve this purpose.

Pedestrians will still enter the lot, in spite of the sign. Just go extra slow if they are there, and perhaps talk to them and ask them to leave. While you won’t actually present a danger to them at your low speed, they probably will heed the advice of 3000lb robots. Perhaps tell them they have 15 seconds to put down their weapon.

Robotic sign?

To get really clever, the sign marking the border of the Robo-Valet area might itself be on a small robot. Thus, when the robo-valet area gets full, the sign can move to expand the area if space is available. You could expand even into areas occupied by human-parked cars — just know that they are there and don’t block them in — or move out of their way when needed. Eventually they leave and only robocars enter.

When the demand goes down, the sign can easily move to shrink the valet area.

A version of this post originally appeared on robocars.com. 

Source: Wikipedia

The runaway trolley has chased automated motor vehicles into the new year.

In early 2012, I raised a variation of the classic thought experiment to argue that there is not always a single absolute right choice in the design of automated vehicles — and that engineers should not presume to always know it. While this remains true, the kind of expert comments that concerned me three years ago have since become more the exception than the norm. Now, to their credit, engineers at Google and within the automotive industry openly acknowledge that significant technical hurdles to fully automated vehicles remain and that such vehicles, when they do exist, will not be perfect.

Unfortunately, the reality that automated vehicles will eventually kill people has morphed into the illusion that a paramount challenge for or to these vehicles is deciding who precisely to kill in any given crash. This was probably not the intent of the thoughtful proponents of this thought experiment, but it seems to be the result. Late last year, I was asked the “who to kill” question more than any other— by journalists, regulators, and academics. An influential working group to which I belong even (briefly) identified the trolley problem as one of the most significant barriers to fully automated motor vehicles.

Although dilemma situations are relevant to the field, they have been overhyped in comparison to other issues implicated by vehicle automation. The fundamental ethical question, in my opinion, is this: In the United States alone, tens of thousands of people die in motor vehicle crashes every year, and many more are injured. Automated vehicles have great potential to one day reduce this toll, but the path to this point will involve mistakes and crashes and fatalities. Given this stark choice, what is the proper balance between caution and urgency in bringing these systems to the market? How safe is safe enough?

When automated vehicles are good enough to reliably replace human drivers across a wide range of driving environments (and we are not there yet), the kinds of incidents that compel a choice among victims will, one hopes, be remarkably rare. In many cases, the prudent strategy for such scenarios will be to drive carefully enough that they either (a) do not arise at all or (b) can be mitigated if they do arise (by, for example, stopping quickly). This is because poor decisions by human drivers— driving too fast, while drunk, while texting, while tired, without braking quickly enough, etc.— contribute to the vast majority of today’s crashes.

In the near term, some crashes might be addressed by automated emergency intervention systems (AEISs) that automatically brake or steer when the human driver fails to act. Because these systems are designed to engage just before a crash (sometimes to lessen rather than to negate the impact), they could conceivably face the kind of dilemmas that are posited for automated vehicles. Nonetheless, some of these systems have already reached the market and are saving lives — as are airbags and electronic stability control and other technologies that necessarily involve safety tradeoffs. At the same time, these intervention systems occasionally detect objects that don’t actually exist (false positives) or fail to detect objects that actually do exist (false negatives).

This is a critical point in itself: Automation does not mean an end to uncertainty.  How is an automated vehicle (or its designers or users) to immediately know what another driver will do? How is it to precisely ascertain the number or condition of passengers in adjacent vehicles? How is it to accurately predict the harm that will follow from a particular course of action? Even if specific ethical choices are made prospectively, this continuing uncertainty could frustrate their implementation.

For this reason, a more practical approach in emergency situations may be to weight general rules of behavior: decelerate, avoid humans, avoid obstacles as they arise, stay in the lane, and so forth. As I note in a forthcoming book chapter (“Regulation and the Risk of Inaction“), this simplified approach would accept some failures in order to expedite and entrench what could be automation’s larger successes. As Voltaire reminds us, we should not allow the perfect to be the enemy of the good.

If you liked this article, you may also be interested in:

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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

The vehicle has a long wheelbase design to allow it to have a very large passenger compartment, which features just four bucket seats, the front two of which can rotate to create face to face seating. (In addition, they can rotate to make it easier to get into the car.) We’ve seen a number of face to face concepts and designs and I’ve been interested in the idea from the start: the idea of making car travel more social and better for both families and co-workers. As a plus, rear facing seats, though less comfortable for some fraction of the population, are going to be safer in a front end collision.

The vehicle features a bevy of giant touchscreens. We see a lot of this, but I will note that we don’t have this at our desks or in our homes. I suspect passengers in robocars will prefer the tablets they already have, though there is the issue that looking down at a tablet generates motion sickness sometimes.

The interior has an odd mix of carpet and hardwood, perhaps trying to be more like a living room.

More interesting, though not on display, are the vehicle’s systems for communicating with pedestrians and other road users. These include LEDs that can indicate if the car is self-driving (boring, and something I pushed to have removed from the Nevada law,) but more interesting are indicators that help to tell pedestrians the vehicle has seen them. In one feature, which only is likely to work at night, a laser projects a crosswalk in front of the vehicle when it stops, in order to tell a pedestrian it sees them and is expecting them to cross in front. It can also make LED words at the back for other cars (something that is I think illegal in some jurisdictions).

Also interesting has been the press reaction. WIRED thinks it’s bonkers and not designed very well. The bonkers part is because the writer thinks it de-emphasizes driving too much. Of course, those of that stripe are quite upset at Google’s car with no controls. Other writers have liked the design, and find it quite superior to Google’s non-threatening design, suggesting the Google design is for regulators and the Mercedes design is for customers. Google plans to get approval for their car and operate it, while Mercedes is just using the F105 as a concept.

I have a gallery of several pictures of the car that I will add to during the week. In the gallery you will also see:

Audio Piloted Driving prototype

Audi drove one of their cars from the Bay Area to CES, letting the press take 100 mile stints. It also helped them learn things about different conditions. One prototype is in the booth, and I will go out to see the real car outdoors tomorrow.

TRW

TRW was showing off their technology with a transparent model showing where they had put an array of radars to make 360 degree radar and camera coverage. No LIDAR, but they will probably get one eventually. Radar’s resolution is low, but they believe that by fusing the radar and the camera views they can get very good perception of the road.

Others

There are more for me to see tomorrow. Ford showed more of their ADAS systems and also their Focus, which has 4 of the 32 plane velodyne LIDARs on it. Toyota showed only a hydrogen fuel cell car. Valeo has some interesting demos I will want to see — they have promised doing a good traffic jam assist. While they have not said so, I think the most interesting car company robocar function will be a traffic jam assist that does not require supervision — ie. you can read.

While no car company is ready to have the driver out of the loop at high speeds, do so at traffic jam speeds is much easier, mainly because you just have to follow the other cars, and you stop self-driving if the jam opens up. Several companies are working on a product like this and I suspect it will be the first real robocar product to reach the market that is actually practical. The “super cruise” products that drive while you watch are pleasant, but not much more world-changing than adaptive cruise control. When a car can give people time back, even if only traffic jam time, something interesting starts happening. 

A version of this article originally appeared on robocars.com.

If you liked this article, you may also be interested in:

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Google just unveiled their first ‘real-build’ self-driving prototype vehicle. This is a follow up to the concept presented in May and signifies the progress of this particular Google project.

According to the short Google announcement, the purpose of this new prototype is testing, initially on a closed test track and then later in 2015 on public roads. Even so, very few actual details are known, beyond those we covered in our previous article for the initial mockup.

The differences between the two cars, however, are actually quite interesting. Google emphasizes the presence of headlamps, which are an indication that the vehicle could be road legal, although it’s not clear to what extent. A glimpse of its frame and construction hinted at a properly engineered vehicle that may be possible to fulfill all the necessary requirements for a regulatory approval (crash tests etc). However, a loophole for this highly experimental vehicle is possible; it may, for example, be regarded as an electric golf cart of sorts.

The composite photo above presents the most obvious differences: a better integrated LIDAR, the headlights and other small details. The most important visible difference is the mirrors. On the first mockup, the mirror fairings were small and so could only accommodate the sensors or cameras of the autonomous system. The new one retains the sensors but adds a proper mirror for the driver. It is almost certain that the car will have manual controls (steering wheel, accelerator and brake pedals) so it can be “supervised” on public roads.

2015 will be an interesting year for autonomous driving. Apart from this purpose-built concept, many manufacturers offer ever more advanced systems, and Google is promoting a modular solution of their own.

• Americans, in particular, will never give up car ownership! You can pry the bent steering wheel from my cold, dead hands.
• I can’t see why anybody would own a car if there were fast robotaxi services available!
• Surely human drivers will be banned from the roads before too long.

I predict none of these extremes will be true. I predict the market will offer all options to the public, and several options will be very popular. I am not even sure which will be the most popular.

1. Many people will stick to buying and driving classic, manually driven cars. The newer versions of these cars will have fancy Advanced Driver Assistance Systems (ADAS) that make them much harder to crash, and their accident levels will be lower.
2. Many will buy a robocar for their near-exclusive use. It will park near where it drops them off and always be ready. It will keep their stuff in the trunk.
3. People who live and work in an area with robotaxi service will give up car ownership, and hire for all their needs, using a wide variety of vehicles.
4. Some people will purchase a robocar mostly for their use, but will hire it out when they know they are not likely to use it, allowing them to own a better car. They will make rarer use of robotaxi services to cover specialty trips or those times when they hired it out and ended up needing it. Their stuff will stay in a special locker in the car.

In addition, people will mix these models. Families that own two or more cars will switch to owning fewer cars and hiring for extra use and special uses. For example, if you own a two-person car, you would summon a larger taxi when three or more are together. In particular, parents may find that they don’t want to buy a car for their teenager, but would rather just subsidize their robotaxi travel. Parents will want to do this and get logs of where their children travel, and of course teens will resist that, causing a conflict.

The current multi-car family is the most likely place for change. Right now, families with two cars use one car (the newer one, typically) more than the other. This is true even if both people in a couple travel the same, because while solo trips are done in each person’s private car, joint trips are usually done in the nicer car. As such, it’s easier to rely on a shared car service for the second car. When it comes to teens, parents resist both the danger a teen is put in by driving, and the high cost of insurance. Superior ADAS should reduce those for manual cars, but the factor will still remain. Parents don’t want to buy the machine that might kill their child, but teens crave mobility — and parents remember their own childhood, and also tire of loaning out their personal car.

Let’s consider couples moving from two cars to one car plus robotaxi service.

In order to give up a car to use robotaxi service, it is necessary that the robotaxi service serve the home and the workplace of those who will shift to it, plus a number of popular destinations. It is not necessary it serve all destinations, unless it is very common that both drivers want to frequently go to non-served locations at the same time. In general, it is best if both workplaces are in the service area, because otherwise the one who works outside the area will take the private car away all day, making it hard for the other to get outside the service area. If both can commute, then they can switch off which person gets the private car that day based on what they plan.

Robotaxi services will be able to get you outside their service area, of course. They will do it by either letting you take the wheel and drive yourself, keeping the car to yourself until you get home, or by taking you to the edge of the service area where you quickly switch into a traditional manually driven carshare vehicle. This should work fairly well, but it’s still a bit of inconvenience and extra cost to do this so one-car couples will prefer to use their private car on these trips.

The next question is, what car do you own as your private car? Today, this is most commonly an SUV, pickup truck or five-passenger sedan. For a couple, a two-person vehicle makes the most sense, and possibly even a two-person “inline” face-to-face vehicle which is very narrow. As a narrow vehicle it is able to move through traffic more readily and store itself easily and cheaply. It would meet most needs, though a bigger vehicle would be summoned for special trips and perhaps long distance trips. The small vehicle may make sense as an electric, once battery costs drop to $300/kwh or less.

Larger families might opt for something larger. Today they often get minivans and SUVs, which are certainly nice if moving multiple kids or going to kids’ activities that need gear and supplies. Speciality vehicles may arise to cater to all the different classes of family needs: the couple with a one small child, the couple with two, the couple with a tween, etc. As cars become easier to swap out — and of course hired robotaxis are swapped out every day — it makes more sense to go for something designed for your vey specific needs rather than something general purpose.

Dealing with your stuff

I am guilty of keeping many things in the trunk of my car. Many of them probably don’t need to be there, but some are things I like to have always available, such as my camera gear and tripods, spare medicines, clothing and gear for unexpected weather or an overnight bag. Some of these needs can be met by delivery robots that could quickly get me an umbrella or warm coat, since the weather does not come on us without warning, but the camera gear I need immediately if there is something surprising to photograph.

For this I have envisioned the “Stuff Depot” concept, found in my 2008 article A week of Robocars. Here, you have a special locker, and your car (or any car you hire) is able to make a quick stop at a depot near you to drop off or pick up this locker. It’s involved, but does answer the question. At the same time, people who live in taxi cities, like Manhattan, just get used to the idea they don’t get to carry a lot of stuff with them at all times, and only keep what they can carry in a handbag or backpack.

The car as status symbol

Many people are attracted to particular cars to show off their status and wealth, or express their style. Some cars are valued because they are expensive, which is a nice racket to get into. Those who buy cars will still be able to display them in their driveways if they so wish, and be seen regularly arriving and leaving in them.

Unlike conventional “hailed” taxis, the density of robotaxi service should be enough to allow varying levels of price, quality and service. One could subscribe to a service that always sends a fancy red Mercedes, for an extra cost. For trips planned with any advance warning, such service could be assured.

Unfortunately there is a reverse effect for on-demand trips. If you want a car on the spur of the moment, you will be told the fancy red Mercedes is 10 minutes away, but a generic small vehicle is one minute away. The luxury service draws from a special fleet, but “first available” can draw from an array of combined fleets — any car in town in your price range. Which do you take becomes a difficult question. Of course, had you thought to request the car 10 minutes before you needed to leave, you could have gotten just what you wanted in this situation, but unless there is a large oversupply of luxury vehicles, there will be times when they are scarce. You can always serve a customer in a vehicle that is more expensive than they paid for, but it’s a different story to serve them a cheaper product, even though that’s generally going to be faster. Will people care more about speed and flexibility, or will they care about luxury and showing off status symbols?

The big thing to note is that it becomes practical to see vehicles with a much greater variety than we do today, particularly if people take the view of “any car if I’m in a hurry, and the particular car I want if I summoned a modest amount of time in advance.” Any market sector that’s big enough can merit a vehicle designed for it. That’s made more true by other trends in manufacturing that make it easier to generate small volume vehicles, such as 3-D printing and modular designs based on electric powertrains.

A version of this article originally appeared on robocars.com.

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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

NEW: Full transcript below.

In this episode, Audrow Nash speaks with Edwin Olson, an Associate Professor at the University of Michigan, about the University’s 32-acre testing environment for autonomous cars and the future of driverless vehicles.

The testing environment, called the “Mobility Transformation Facility,” has been designed to provide a simulation of circumstances that an autonomous car would experience driving on real-world streets. The Transformation Facility features “one of everything,” says Edwin Olson, including a four-lane highway, road signs, stoplights, intersections, roundabouts, a railroad crossing, building facades, and even, mechanical cyclists and pedestrians.

Edwin Olson
Edwin Olson is an Associate Professor of Computer Science and Engineering and the University of Michigan. He is the director of the APRIL robotics lab, which studies Autonomy, Perception, Robotics, Interfaces, and Learning. His active research projects include applications to explosive ordinance disposal, search and rescue, multi-robot communication, railway safety, and automobile autonomy and safety.

In 2010, he led the winning team in the MAGIC 2010 competition by developing a collective of 14 robots that semi-autonomously explored and mapped a large-scale urban environment. For winning, the U.S. Department of Defense awarded him $750,000. He was named one of Popular Science’s “Brilliant Ten” in September, 2012. In 2013, he was awarded a DARPA Young Faculty Award.

He received a PhD from the Massachusetts Institute of Technology in 2008 for his work in robust robot mapping. During his time as a PhD student, he was a core member of their DARPA Urban Challenge Team which finished the race in 4th place. His work on autonomous cars continues in cooperation with Ford Motor Company on the Next Generation Vehicle project.

Links:

• Download mp3 (16.4MB)
• Subscribe to Robots using iTunes
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• Report on the Mobility Transformation Facility
• Edwin Olson’s website

SMART-NUS enhanced driverless buggy. The steering wheel has been removed and replaced with an in-vehicle touchscreen for alternate selection of destination. Researchers will shadow the buggy on electric bicycles during the demo.

Robocar R&D is moving fast in Singapore, and this week, the National University of Singapore (NUS) announced they will be doing a live public demo of their autonomous golf carts over a course with 10 stops in the Singapore Chinese and Japanese Gardens. The public will be able to book rides online, and then summon and direct the vehicles with their phones. The vehicles will have a touch tablet where the steering wheel will go. Rides will be free, and will take place Oct. 23-25, Oct. 30-31 and Nov. 1.

This is not the first such public demo – the CityMobil2 demonstration in Sardinia ran in August on a stretch of beachfront road blocked to cars but open to bicycles, service vehicles and pedestrians. Where the Sardinia project offered only a linear route, the Singapore project press release includes maps showing that the route is non-linear. Both projects have very low maximum driving speeds of 10km/hour.

The Singapore project will also mix with pedestrians, but the area is closed to cars and bicycles. (They have previously demonstrated not just detecting pedestrians but driving around them – provided they stay still, but it’s not clear whether the current project includes that ability). There will be two safety officers on bicycles riding behind the golf carts, able to shut them down if any problem presents, and speed will also be limited.

Singapore is interesting because they have a long history of transportation innovation, and good reason for it. As a city-state, it’s almost all urban, and transportation is a real problem. That’s why congestion charging was first developed in Singapore. Other innovations have also been started there; for example, every vehicle in Singapore has a transponder, and they used for a variety of purposes including congestion tolling and seamless parking payment in almost all parking lots.

Six months ago I had the chance to visit A*STAR, Singapore’s government funded national R&D centre, as well as the car project at the National University of Singapore (NUS). Then in late August, I visited Singapore again to give an address at a special conference announcing a government-sponsored collaboration involving their Ministry of Transport, the Land Transport Authority and A*STAR. I had a chance to meet the minister and sit down with officials to talk about their plans, and at the conference, there were demos of vehicles, including one from Singapore Technologies, which primarily does military contracting.

In spite of its history, Singapore tends toward conservatism — and this might dampen their pace of innovation. While this joint project is a good start, I advised them that, in my view, private projects will be able to move faster than public sector ones.

The NUS project is a collaboration with MIT, involving professor Emilio Frazzoli.

A version of this article originally appeared on robocars.com.

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The idea of autonomous cars that can communicate with each other and organize themselves to better control the flow of traffic sounds interesting and possibly scary at the same time. In our latest poll, the Open Roboethics initiative and Robohub wanted to find out whether people are supportive or skeptical of the idea of such network of cars.

Results of the poll shows that most people see such cars to save time for them and reduce the number of collisions, because the network of cars will be orderly and able to decrease the chance of you getting stuck in traffic jams. These two reasons echo our findings from the very first poll Open Roboethics initiative and Robohub have published on the topic of autonomous cars in May. When we asked about the main advantages of owning an autonomous car, “reduced number of traffic accidents” and “saving of time that would have otherwise been spent on driving” were the two most popular answers.

When asked about the potential harms of such network of autonomous cars, the most popular answer (53%) was that it may lead to invasion of privacy, followed by 45% of participants expressing the concerns that accidents can happen from technical glitches and 39% expressing decrease in safety due to the potential of the cars being hacked or abused. From this result, can we say that more people are worried about negative aspects what may come incorporated into such cars by design (privacy issues and technical glitches) than the possibility of a third party (a hacker) hacking or abusing the system? We are not quite ready to make such statement with the limited data we’ve collected from this poll. But given that hacking a car has recently been reported as being fairly easy, people’s worries about the possibility of autonomous car hacking may change in the future.

Overall, though, it seems that people are quite open to the idea of cars that can communicate and self-organize. Only 15% of participants said no to such technology, whereas 85% said yes. This is an optimistic result for a potential technology that may come after we’ve adopted autonomous cars in our everyday lives already. But people’s perception on this may change depending on how well autonomous cars without the communicative, self-organizing feature fares.

When we asked our readers, ‘should the unlicensed be allowed to ride in autonomous cars alone?’ we found some surprising results. While over half of the participants (52%) were opposed to having children ride in autonomous cars alone, a majority of participants were supportive of unlicensed seniors and legally blind persons riding in the cars alone (67% and 58% respectively).

This made us curious about some of the key factors that may be at play in answering the question of who should be allowed to ride in autonomous cars. More specifically, how do people feel about autonomous cars driving around the city streets without a passenger? What if the passenger is drunk or under the influence of drugs? We explored this topic in our latest poll.

Should a fully autonomous car be able to drive around city streets without a human passenger?

If people have genuine distrust toward autonomous cars, then we should expect them to oppose the idea of autonomous cars driving around without a passenger — regardless of the added convenience from a car that drops you off at your destination and goes off by itself to park.

But our results suggest that we don’t really have the issue of pure distrust toward autonomous cars. The majority of participants (80%) supported the idea of a car driving by itself without a passenger, while only 11% opposed the idea. Some of those falling in the ‘other’ category conditionally supported this:

“Not without purpose. i.e., If someone ordered a car then yes, otherwise no.” (Participant 43)

Should an adult under the influence of drugs or alcohol be allowed to ride in autonomous cars?

When we asked our participants whether an adult under the influence of performance impairing substance (e.g., drugs or alcohol) should be allowed to ride an autonomous car alone, the result showed drastic difference depending on whether the car requires human input or not. In general, if the car never requires human input, 94% of participants were supportive of an adult riding in autonomous cars regardless of whether the person is drunk or under the influence of drugs. On the other hand, a majority of participants (77%) opposed this idea if the car sometimes requires human input.

In short, it seems people trust the autonomous controls of a car more than a human passenger if drugs or alcohol are in the mix. Given this and our earlier results it seems that, perhaps unsurprisingly, decision making abilities and the level of autonomy of the car are major factors that should be taken into account when it comes to your qualifications to ride in an autonomous car alone.

It is interesting, however, that more people are supportive of a drunk or high passenger riding in a fully autonomous car (one that never requires human input) than having an autonomous car roam the streets without any passengers. From passenger safety point of view, an empty car that gets into an accident would not put any passenger’s life in danger — whether the accident involves physical harm to pedestrians would depend on the accident. Perhaps this irony stems from the fact that having a passenger inside a car would make it easier to hold a person (likely the passenger) responsible for accidents.

Regardless of the reasons for this contrasting result, one thing is clear: even when we have autonomous cars, drinking and driving is going to be a tough sell!

The results of the poll presented in this post have been analyzed and written by AJung Moon, Jason Millar, Shalaleh Rismani, and Camilla Bassani at the Open Roboethics initiative.

The conservative view sees this technology as a set of wheels that has a computer.

The aggressive school sees this as a computer that has a set of wheels.

The conservative view sees this as an automotive technology, and most of them are very used to thinking about automotive technology. For the aggressive school, where I belong, this is a computer technology, and will be developed — and change the world — at the much faster pace that computer technologies do.

Neither school is likely to be entirely right, of course. It won’t go as gung-ho as a smartphone, suddenly in every pocket within a few years of release, being discarded when just 2 years old even though it still performs exactly as designed. Nor will it advance at the speed of automotive technology, where electric cars are finally getting some traction a century after being introduced.

The conservative school embraces the 4 NHTSA Levels or 5 SAE levels of technology, and expects these levels to be a path of progress. Car companies are starting to sell “level 2” while working on “level 3” and declaring level 4 or 5 to be far in the future. Google is going directly to SAE level 4.

The two cultures do agree that the curve of deployment is not nearly-instant like a smartphone. It will take some time until robocars are a significant fraction of the cars on the road. What they disagree on is how quickly that has a big effect on society. In sessions I attended, the feeling that the early 2020s would see only a modest fraction of cars being self-driving meant to the conservatives that they would not have that much effect on the world.

In one session, it was asked how many people had cars with automatic cruise control (ACC.) Very few hands went up, and this is no surprise — the uptake of ACC is quite low, and almost all of it is part of a “technology package” on the cars that offer it. This led people to believe that if ACC, now over a decade old, could barely get deployed, we should not expect rapid deployment of more complete self-driving. And this may indeed be a warning for those selling super-cruise style products that combine ACC and lanekeeping under driver supervision, which is the level 2 most car companies are working on.

To counter this, I asked a room how many had ridden in Uber or its competitors. Almost every hand went up this time — again no surprise. In spite of the fact that Uber’s cars represent an insignificant fraction of the deployed car fleet. In the aggressive view, robocars are more a service than a product, and as we can see, a robocar-like service can start affecting everybody with very low deployment and only a limited service area.

This dichotomy is somewhat reflected in the difference between SAE’s Level 4 and NHTSA’s. SAE Level 4 means full driving (including unmanned) but in a limited service area or under other limited parameters. This is what Google has said they will make, this is what you see planned for services in campuses and retirement communities. This is where it begins, and grows one region at a time. NHTSA’s levels falsely convey the idea that you slowly move to fully automated mode and immediately do it over a wide service area. Real cars will vary as to what level of supervision they need over different times, streets and speeds, possibly even existing at all the levels at different times.

Follow the conservative model and you can say that society will not see much change until 2030 — some even talk about 2040. I believe that is an error.

The two approaches will also clash when it comes to deciding how to measure the safety of the products and how they should be regulated, which will be a much larger battle. More on that later.

This post originally appeared on Robocars.

Given a choice between crashing into a motorcyclist wearing a helmet vs. a motorcyclist who isn’t wearing one, which one should an autonomous car be programmed to crash into? What about the choice between crashing into an SUV vs. a compact car?

These are some of the dilemma situations Professor Patrick Lin brought forth in his WIRED article, The Robot Car of Tomorrow May Just be Programmed to Hit You.

Lin says that programming a car to collide with any particular kind of object over another seems like a targeting algorithm. Accidents involving human drivers typically involve people’s split-second, instinctive responses to the situation. Your first autonomous car of the near future, however, will probably come equipped with a crash-optimization algorithm – a deliberately designed algorithm that will determine the outcome of all potential crashes. As Lin points out in his article, the pre-meditated nature of such algorithm creates a load of interesting issues that calls for a broader discussion on the topic.

So two weeks ago we, the members of the Open Roboethics initiative and the Robohub family, decided to help continue the discussion by conducting our reader poll on this very topic.

Scenario 1: Motorcyclists with / without a helmet

Given the choice between the two motorcyclists (one wearing a helmet, and one not), we asked our readers who an autonomous car should crash into. Choosing to hit the helmet wearing motorcyclist would perhaps minimize overall harm done. But considering the perspective of the motorcyclist who took that extra few minutes to put on a helmet for safety reasons, such programmed decision by an autonomous car doesn’t seem fair. Indeed, only 2% of our participants said that the car should crash into the biker wearing a helmet because s/he has better chances of survival. Lin points out that choosing to hit the biker wearing a helmet might discourage bikers from wearing helmets.

Crashing into the biker not wearing a helmet wasn’t a popular choice either (10%). In fact, our most popular response (45%) was that the car should hit the brakes and do nothing else, leaving it up to chance to determine which biker gets hit. And 3% of participants provided us with a similar response, where they said the car should run a random number generator to make a random decision. This means that almost half (48%) of our participants advocating for random chance to decide the outcome of the crash.

Relatedly, 30% of our participants said that the car shouldn’t have the ability to detect whether a biker is wearing a helmet or not. Given that a decision made by random chance does not make intelligent use of sensed data, effectively 78% of our participants are voting for autonomous cars to not use certain sensed data in making crash decisions.

But not using available information in crash decisions doesn’t seem like a straight forward solution to crash-optimization. Lin says,

“Not using that information in crash-optimization calculations may not be enough. To be in the ethical clear, autonomous cars may need to not collect that information at all. Should they be in possession of the information, and using it could have minimized harm or saved a life, there could be legal liability in failing to use that information.”

Scenario 2: Hit the SUV or a compact car?

An odd sense of unfairness also exists if we consider another crash scenario. Let’s say the car has a choice between an SUV and a compact car to unavoidably crash into. The car could be programmed to hit the SUV over a compact car, since bigger cars with perhaps better safety ratings could better absorb impact from the collision and minimize overall harm from the crash.

The responses we got are similar to the first scenario: 37% opted for the car to simply engage the brakes and do nothing else regardless of which car gets hit, and 25% said that the cars shouldn’t have the ability to detect the make/model of the cars around it.

A slight difference from the first scenario is that 20% said that the car should crash into the SUV (a minimum overall harm option), and only 3% said the car should crash into the compact car. In the first scenario, the option that yields minimum overall harm (hit the motorcyclist with a helmet) only received 2% support as compared to 20%.

This might have something to do with the fact that people’s decision to drive a certain make or model of a car is not illegal, nor an activity that typically increases risks of driving. So no one is really doing anything wrong by driving a compact car or an SUV, whereas the motorcyclist wearing a helmet has a moral/legal high ground over the other motorcyclist.

Regardless, if we were to have crash-optimization algorithms that biasedly crash into SUVs over smaller cars, it will surely have an impact on the consumer market. The rates of insurance for these supposedly more safe cars may go up, because buying a safer car may also mean a higher probability of being crashed into by an autonomous car – yikes!

Is it OK for a car to always choose to crash into non-law-abiding citizens?

So how does moral or legal high ground of individuals on the road impact people’s decisions about who should be hit? We asked our readers whether it’s OK for a car to always choose to crash into those not following traffic laws over those who are, assuming that the car can detect it. Considering the first scenario, it is illegal to ride a motorcycle without a helmet in many countries/states. According to our results, law-abiding status of people on the road doesn’t seem to be a popular variable to optimize for crashes. The majority of respondents said it is not OK (70%), whereas only 20% said it is OK for cars to biasedly crash into non-law-abiding citizens.

It is true that by answering ‘yes’ to this question, you’d have to be OK with the idea that the car might always choose the one less likely to survive from a crash. Although we have traffic laws in place to regulate the rules of the road and maintain social order, using it to make life/death decisions in this manner may not be something people are comfortable with.

How should an autonomous car respond to unavoidable crashes?

So how should an autonomous car respond to unavoidable crashes? Is there a general rule that people are more in support of? According to our results, majority of our participants (52%) are in favour of minimizing overall harm to both pedestrians and passengers by spreading out harms. But it seems that the rest of the participants are quite divided: 20% of people support to minimize harm to pedestrians at the expense of passengers, while 13% supports the opposite.

This reminds us of a recent reader poll discussion we had on a different crash dilemma situation, in which a large number of participants (64%) said an autonomous car should save the life of its passenger over a child on the road (36%). One of the main reasons for choosing to save the passenger’s life was the notion that a car should always have its passenger’s safety as its priority over that of others. Given that the previous poll result provided such a strong support for prioritizing the passenger’s safety, it is surprising to see a contrasting result on this poll.

To find out what makes people prioritize passenger safety over pedestrians or vice versa, we’ll have to do some more detailed investigations. What is also interesting is that, although people preferred autonomous cars to take random or uninformed decisions in the two specific unavoidable crash scenarios above, the majority of people opt for minimizing overall harm to both pedestrians and passengers – an option that is likely to be the least random and in need of using the most amount of information about the passenger and pedestrians.

For now, it seems that there is much more discussion to be had before autonomous cars start making crash decisions people will be happy with. Check out our latest reader poll: When fully autonomous cars are the norm, will you miss driving?.

The results of the poll presented in this post have been analyzed and written by AJung Moon, Camilla Bassani, and Shalaleh Rismani at the Open Roboethics initiative.

If you liked this article, you may also be interested in:

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• If death by autonomous car is unavoidable, who should die? Reader poll results
• Should autonomous cars be allowed to speed? Results from our reader poll
• Kids with wheels: Should the unlicensed be allowed to ‘drive’ autonomous cars?
• What should a robot do? Designing robots that know right from wrong
• Morgan Stanley reports on the economic benefits of driverless cars

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

This article is an excerpt from a book chapter by Bryant Walker Smith titled A Legal Perspective on Three Misconceptions in Vehicle Automation appearing in Road Vehicle Automation (Springer, 2014, Eds. Gereon Meyer and Sven Baker), and considers only one of the three misconceptions addressed in the full chapter.

Casual consumers of automated vehicle news—though not the many casual producers of this news—could be forgiven for concluding that driverless cars are ready to be sold to ordinary drivers. States are passing laws, companies are testing cars on public roads, and commentators are declaring that “the technology is here.” The corollary of this belief is that, if such vehicles are not yet ready, then fault must lie elsewhere—with consumers for not accepting them, with governments for not “legalizing” them¹, or with lawyers for outright blocking them.

A 2013 radio interview is illustrative: The first guest, a reporter, asserted that “the technology is here” and that “right here and now we can have driverless cars.”

I replied that the research vehicles under discussion were neither designed nor demonstrated to operate at a reasonable level of risk under a full range of unsupervised driving scenarios.

After a short break, the host resumed the discussion by reminding listeners that “the technology for driverless cars is in fact available, and we’re trying to figure out why we don’t then have them.”

Automotive experts recognize that the path from research to product is long—and that there is a tremendous difference between, on one hand, a research system that well-trained technicians carefully maintain, update, and operate exclusively on certain roads in certain conditions and, on the other hand, a production system that poorly trained consumers neglect and abuse for two decades in almost any conceivable driving scenario. For this reason, production vehicles take years to be developed, tested, and certified to a complex array of highly detailed public and private standards.

Recent state laws regarding automated driving embrace this important distinction between research-and-development testing and consumer operation: Nevada’s infinity-styled “autonomous vehicle” license plates, for example, are red for test vehicles and, one day, will be green for all others.

However, a yellow license plate may, at least metaphorically, be most appropriate for a set of potential deployments that do not fit comfortably in either category. The first deployments of low-speed shuttles² are likely to be pilot projects.

Volvo Cars intends to place 100 automated vehicles on public roads in the Swedish city of Gothenburg by 2017³. Internet companies that are comfortable with invitation-only beta rollouts of their software and hardware may adopt a similar approach for their updatable automotive products. And an individual who uses a vehicle that she herself has built or modified may likewise straddle the divide between testing and operating.

These hybrid deployments may push up against state and federal regimes that assume a more straightforward product path for research, development, production, sale, resale, and disposal. For example, while automakers currently self-certify that their vehicles as originally manufactured meet federal safety standards, this date of original manufacture may be less determinative of the safety of vehicles subsequently modified. Similarly, while state tort law often looks to the date that a product is originally sold to a consumer, as a practical matter this date may become less clear or less relevant to alleged harms.

Indeed, automakers concerned about the post-sale modification of their vehicles by third parties have lobbied successfully in Florida and Michigan (and unsuccessfully in California) to expressly limit their liability for injuries caused by such modification. These statutory provisions, however, largely restate existing principles of tort law, which makes both the insistence on and the opposition to them rather striking.

The complete lifecycle of early automated vehicles does present significant concerns. The mechanical life of these vehicles may be much longer than the functional life of their automation systems. Consumers in the secondary market may face a hodgepodge of proprietary driver assistance systems with different capabilities and limitations that cannot be easily intuited. And vehicles may long outlive some of the companies—whether small startups or legacy behemoths—responsible for their design, sale, and ongoing support.

For these reasons, what I have called “planning for the obsolescence of technologies not yet invented” should be a key consideration for automakers, regulators, and insurers.

Robohub is an online platform that brings together leading communicators in robotics research, start-ups, business, and education from around the world. Learn more about us here. If you liked this article, you may also be interested in:

• SAE defines six levels of driving automation
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• How do self-driving cars work?

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

In this episode, we speak with Kyle Vogt, the CEO of Cruise. His company recently joined the “driverless revolution” with their release of RP-1. This system is a highway autopilot that can be installed in your existing car. It controls your steering, throttle, and braking, making sure your car remains safely in its lane and a safe distance from the car in front of you.

The Cruise RP-1 sees the road and cars around you through the sensor pod mounted on the roof of your car. Through a combination of cameras, radar, and other measurement systems, the Sensor Pod relays what it detects back to the Cruise Computer to make real time decisions on where to go. A click of a button will enable the RP-1 to take control of steering and speed.

[youtube.com/watch?v=uEQqQj_zyHI]

Kyle Vogt

Kyle is the CEO and Founder of Cruise, a company building driving automation technology.

Previously, Kyle was a Co-Founder at Justin.tv, Socialcam (acquired by Autodesk), and Twitch. Kyle studied computer science and electrical engineering at MIT. 

Links:

• Download mp3 (10.1MB)
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• Cruise Website

It may look amusing and easily dismissed as a novelty prototype many years from production, but take a good look at a technology that in a few short years is going to transform your life and have major implications for your business.

Last month Google officially announced at the inaugural Code conference that they have built their very own prototype self-driving car. It is designed to be used in urban settings at up to 25mph. It has foam bumpers and an electric motor and, for now at least, the Google signature spinning LiDAR on top. But what really sets it apart is what it doesn’t have: no steering wheel, accelerator nor brake pedal.

A video of the car was also posted on the official Google blog, that included some sample riders from the key demographics that will benefit the most from this technology: disabled (Steve Mahan, star of a previous video and 95% blind), some seniors and a mom and her son.

Change How and Where We Live and Work

The reason that this is such a big deal for your business is because the simple fact that a car can drive itself is going to change how and where we all live and work. The vehicle can dynamically position itself to the point of need and then do work by moving people or goods to where we instruct. We will have a limitless supply of on-demand chauffeurs, or fully automated taxis if you prefer.

We therefore don’t really need to own a vehicle anymore if we don’t want to; or even own several as the automaker’s business model would prefer. Rather than have our multi-purpose vehicle sit idle some 90% of the day, we can hire a vehicle suitable for our need; a shared self-driving electric 2-seater for our commute (and work on the way); an SUV for the group trip at the weekend.

Some will prefer to live further from the city as the commute can now become part of their working day. Some will find it even easier to live in the urban center as they need never own a car again. Sprawl and urban densification at the same time; that will create enormous money flows from many existing business models to new business models. The impacts on land values alone could be transformative.

A few offices will go mobile as rents and overhead costs in the city mean that some prefer the efficiencies and benefits of travelling between job-sites or appointments in their self-driving office.

Have you considered a pimped-out self-driving RV as part of your retirement plan? Travel North America like a snow-bird moving from region to region with the weather and enjoying the company of your like-minded itinerant community.

Some will find their jobs start to change or even disappear such as professional drivers (taxi, truck, bus etc), auto-body repair, auto-insurance, road safety professionals, transportation planners etc.

For some new jobs will appear as new business models spring up overnight. This novel technology will be the catalyst for so many new societal interactions and services. Retail outlets will discover just how useful and cheap self-driving vehicle logistics are. When the vehicles are re-positioning for the morning peak hour they will be able to carry shopping to our homes in journeys that the empty vehicles would have done anyway. You will be able to use social media to profile the characteristics of your ride-share partner for your planned journey, as it’s a great way to cut costs and make new friends. You could even use the new self-driving ‘match’ service, and if the morning drive goes well you can go out on a date later that evening.

The hospitality industry will forever be in debt to this technology if it means more people are willing to get out for the evening and have a good time because they are less concerned about how they will get home.

If you are having problems finding workers then you will be pleased to know that self-driving vehicles will greatly improve access to your business premises. Some that are disabled, too poor, without a driving license, unable to drive etc. will now find it that much easier to hold down a job.

Planning is Prudent

If you are making medium and long term plans for your business then please consider how self-driving vehicles might impact on them. For instance, if you are going to commit billions of dollars to some major transportation infrastructure then you might want to do some due diligence, as the optimal project design could look very different in ten years time when self-driving cars are in the midst of transforming society. At a more mundane level, if you are expanding your business and wondering how you are going to accommodate all that extra parking in the future, then perhaps you may not need to.

Morgan Stanley are certainly encouraging us to take this technology seriously. They estimate that the US could save $1.3 trillion/year when self-driving vehicles are fully deployed; that is nearly 8% of the US GDP in 2012.

To misquote the British military 7P’s adage: Your proper prior planning for the implications of self-driving vehicles will help prevent poor performance; and is very prudent.

Is this for real?

From collating all of the recent statements from the automakers and developers there is a very high level of confidence that fully self-driving cars will be on our roads by 2025 at the very latest. Mercedes, Nissan and Volvo are all indicating a high level of autonomy by 2020, and Google have indicated their aspiration to have their technology in public hands in various possible forms sometime between 2015 and 2019.

Finally, if you are still skeptical about this whole subject and really aren’t concerned for your business, then please consider what Warren Buffet has to say given his significant Geico investment:

“That is a real threat to the auto insurance industry,” Buffett said at his company’s annual meeting here Saturday. “If [self-driving cars] prove successful and reduce accidents dramatically, it will be very good for society and very bad for auto insurers.”

Warren’s not laughing, and neither should you be.

If you liked this article, you may also be interested in:

• Pure autonomy: Google’s new purpose-built self driving car
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See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

“When the American people, through their Congress, voted for a twenty-six-billion-dollar highway program, the most charitable thing to assume is that they hadn’t the faintest notion of what they were doing.” So warned Lewis Mumford in 1958, two years after the inauguration of the U.S. Interstate Highway system. “Within the next fifteen years,” Mumford added, “they will surely find out.” The following decades indeed proved Mumford right.

As the “preposterously unbalanced program” pursued the utopian mirage of a city in which anyone could drive anywhere, anytime, and park at the destination, it destroyed much of the urban America it was meant to serve. Since the 1930s, as state highways funded by gasoline taxes entered cities, packed surface parking lots and parking garages erased much of America’s urban space.  The urban interstates accelerated the trend. At their worst, they were “pyramid building with a vengeance: a tomb of concrete roads and ramps covering the dead corpse of a city.”¹ If the consequences were not always so bad, it’s because local residents stopped some of the most destructive projects, and because some resourceful communities pursued alternative futures.

Autonomous vehicles can correct for these excesses—or exacerbate them. Getting this right will demand recovering a history we’ve lost, and escaping a history written for us by those with a stake in it.

Solving the Wrong Problem

Such destructive extravagance was sold as the rational product of applied science. It was indeed substantially rational in its methods, many of which were so routinized as to be almost automatic in their application: traffic demand measurements, level of service grades, and functional classification systematized highway engineering processes. But routinized programs can only implement solutions to the problems humans define.

Ludwik Fleck, the Polish scientist and philosopher of science, explained that “the formulation of a problem already contains half its solution.”² If we misstate the problem, we may preclude the best solution. The more effectively and the more zealously we solve the wrong problem, the worse the result. When bleeding and purging patients didn’t work, eighteenth-century doctors bled and purged them more—to the detriment of their patients. They got the problem wrong. To them, the problem was “how do we cleanse the blood and restore balance to the humors?” The destruction of American cities to accommodate automobiles, the urban sprawl surrounding these cities, and the loss of alternatives to driving were not the wrong solution. They were the right solution to the wrong problem. Autonomous vehicles can treat these symptoms or exacerbate them, depending upon how we formulate the problem.

In autonomous vehicles and other intelligent transportation systems, we may have a solution so powerful that we fail to pause and ask what problem such systems are best suited to solving. We may fail to ask whether the problem formulation we inherited is the right one. We may justify an emphasis on autonomous cars out of a misreading of history that tells us that we must begin with the assumption that Americans prefer to drive. Above all the neglect of history—or what is much the same, the uncritical acceptance of agenda-driven histories others have packaged for us—may deprive us of hard-earned experience. Experience is the parent of judgment. How strange, then, that so often we find innovators contending that innovation negates the validity of experience: “this changes everything.” Our solutions may be new but the problems of society—the problems of living well together—ultimately are not.

The problem formulations we inherit are the products of history. Born in the 1930s, accelerating to a crescendo in 1956, and persisting ever since, the dominant American surface transportation problem has in effect been: How can motorists be permitted to drive wherever and whenever they want, and to park at their destinations, with minimal delay and with reasonable safety?  In rural America this problem formulation was not unreasonable. In cities, however, it began as a revolution against older formulations that were antagonistic to automobiles.³ Because the private motor vehicle’s spatial demands made it incompatible with density, the new formulation had profound implications for cities.

Autonomous vehicles will be a powerful tool. But they cannot tell us what purpose they will serve.  The time to ask is now, before autonomous vehicles become common.

If we retain the problem formulation of the mid twentieth century, autonomous cars will offer us a better solution to the wrong problem.  Each car will safely travel faster, require less lane space (width and length), require less fuel, make better use of existing road and parking capacity, and demand much less attention from the driver. If total driving (vehicle miles of travel—VMT—per person) remained constant, the achievement would be spectacular.  But under the prevailing problem formation, per person VMT would not remain constant.

If engineers continue to seek to accommodate all of motorists’ demands, and if they accommodate such demands much more efficiently, each car may make much more efficient use of road and parking capacity, but total demands may rise so much that even more space will be needed for road surface and for parking. In the fully autonomous vehicle, as the driver need pay little or no attention to driving, driving time may become work or play time—in effect negating the time cost of travel. Autonomous cars might also safely travel much faster. Such changes might turn the 50-mile commute of today into the 100-mile commute of tomorrow. Today, people trying to travel by other modes—such as walking or bicycling—must contend with urban sprawl governed drivers’ perceptions of distance. How will they reckon with distances that have doubled again?  Presumably many of them too will resort to driving. However reluctantly they turn to it, their decision will be taken as a vote for driving.  Finally, as the skill demands of driving fall we’ll have more drivers. Such trends would mean that we would continue to rebuild the world for drivers, instead of asking what world we want to live in and conforming driving to it.

In 1865 William Stanley Jevons cautioned that more efficient steam engines would not conserve Britain’s coal supply; instead they would increase total national coal consumption because more enterprises would make more use of the more efficient engines.⁴  More recently, “Jevons’ paradox” has been independently rediscovered by each web user, who finds that faster connection speeds mean more total time spent online, not less. If intelligent systems accelerate vehicular “connection speeds,” Americans may spend more total time in vehicles, use them for even more tasks, and negate the space and fuel savings that autonomous vehicles might offer through a different problem formulation. Given difficult facts such as climate change, depletion of fossil fuels (or recourse to fracking, tar sands, and other controversial means of supply), urban sprawl, and the public health costs of physical inactivity, such trends would be cause for alarm.

History and Problem Formulation

In averting these trends, history is our greatest ally—and our greatest threat. History legitimizes some problem formulations, and in so doing delegitimizes others. Parties with agendas to advance tell versions of history that serve these agendas. But impartial histories, pursued with care by researchers with no financial stake in the result, and who welcome correction from each other, can expose and excise false formulations.

The most influential histories we have inherited were designed to serve agendas, however. For example, one of the first defenses of any effort to accommodate all driving demands at any cost is the assertion that Americans prefer driving.  More precisely, history allegedly shows that Americans in general have an unconditional preference for driving over alternatives.  Critics and defenders of the automobile agree that Americans have had a long “love affair with the automobile.” For the critics, the love affair is a misguided infatuation from which Americans must be rescued; for the defenders, the love affair is the free preference of a free people and must be honored as such.  But both agree that Americans prefer driving.

Yet no one on either side of this debate seems to recognize that the “love affair” metaphor is not a sociological fact, finding organic expression in a term of folk wisdom.  It was an invention propagated to serve a purpose. Introduced in 1958 in a criticism of the automobile industry (John Keats’ The Insolent Chariots), the phrase was readapted as a defense of the automobile in 1961 in a one-sided television history of the American car. Millions saw “Merrily We Roll Along,” an episode of NBC’s Du Pont Show of the Week (Du Pont owned a 23 percent share in General Motors, to which it sold varnishes and fabrics⁵). Host Groucho Marx told Americans the history of the automobile in the United States as a love affair between the American man and the (feminized) automobile. Love is blind; social critics can find fault with it but in a free society the will of the people must be honored. And love will find a way—whatever the costs.  In this little drama, critics of the automobile’s excesses become outdated, prudish chaperones.

Though its origins were soon forgotten, the love affair thesis and similar stories overwhelmed older stories of the automobile in the American city—many of them characterizing the car not as love object but as despised intruder.  Nevertheless the love affair thesis remains the dominant explanation of Americans’ extravagant accommodation of the automobile. If there is an official history of the automobile in the United States, this is it. Since 2003, visitors to the Smithsonian’s National Museum of American History in Washington, D.C., in the General Motors Hall of Transportation, have found the love affair thesis in exhibit form. Besides GM, major sponsors of “America on the Move” include the American Automobile Association, State Farm, and ExxonMobil; support from the American Public Transportation Association, the American Road and Transportation Builders Association, the Association of American Railroads, the National Asphalt Paving Association, and UPS is also noted. Acknowledging some of the costs of accommodating the automobile, the exhibit attributes the car’s dominance overwhelmingly to mass preference: in a free market of transportation modes, “Americans chose … the automobile.”⁶

The love affair thesis is essentially a free choice thesis. It is history written by those with a stake in history. It is an ex post facto justification. It is not an outright falsehood. Such lies seldom work. Instead it is a tactical distortion that sidelines appeals to common sense by appeal to a greater power—love. What’s lost in this stratagem is the negation of choice in an environment lacking good alternatives. Lock a man in a 7-11 for a week and study his diet. If we find that he eats a lot of processed food products, we can’t conclude that he has a thereby expressed an unconditional preference for them. Urban Americans chose the car only after a protracted struggle for control of the formulation of traffic problems—a struggle in which many of them fought on the losing side, and which the victors were a minority.⁷ Americans still choose the car—but they do so in an environment deprived of good alternatives. They choose not in a free market of equal alternatives, but in an environment in which drivers don’t pay the full cost of accommodating cars and in an environment built to accommodate cars at all costs.

Beyond Good and Evil

The love affair thesis, by which the car is always “good,” invites an equally fatuous rejoinder: the car is always “bad.” Too many have accepted the invitation. Those who do unwittingly support the love affair thesis by accepting its premise—that cars are either good or bad. Like any useful tool, the car has purposes to which it is ideally suited. It is particularly well suited to low-density areas and as a connector between transportation hubs. As a taxi or shared vehicle it can also be very useful in dense cities. But those with an interest in promoting cars succeeded in casting America’s transportation problem not as a problem of moving people but as a problem of accommodating cars everywhere—even to the point of rebuilding cities for cars. In so doing they diminished Americans’ range of transportation choices, as if their free choices could not be trusted.

Autonomous vehicles can serve the right solution to the right problem. In a city built for primarily people, and in which vehicles serve people, autonomous cars can valuably supplement integrated transportation systems in which each mode is suited to its purpose, in which transportation modes serve the city (not the other way around), and in which transportation success is measured by people moved and not by vehicles moved. They can serve a city in which more needs can be met without vehicles, in which vehicles can be shared, in which walkable distances can be conveniently walked, in which bicycles can connect homes and shops, and in which mass transportation services can make a virtue out of density. But before we make autonomous cars the solution, we must formulate the problem correctly. Until we do, we risk accelerating a journey to the wrong destination. If we rebuild the landscape for autonomous vehicles we may make it unsuitable for anything else—including walking. What kind of city do we want—and what kind of mobility will serve it best? Before we uncritically accept the answers we inherited from the past, let us reexamine the question.

[1] Mumford, “The Highway and the City,” Architectural Record 123 (April 1958), 179-86.

[2] Fleck, Genesis and Development of a Scientific Fact (1935; University of Chicago Press, 1979), 37.

[3] Peter D. Norton, Fighting Traffic: The Dawn of the Motor Age in the American City (MIT Press, 2008).

[4] Jevons, The Coal Question: An Inquiry Concerning the Progress of the Nation, and the Probable Exhaustionof Our Coal-Mines (London: Macmillan, 1865).

[5] Robert F. Freeland, The Struggle for Control of the Modern Corporation: Organizational Change at General Motors, 1924-1970 (Cambridge University Press, 2001), p. 4.

[6] “What Happened to Streetcars?” (exhibit sign), America on the Move, National Museum of American History.

[7] Norton, Fighting Traffic.

If you liked this article, you may also be interested in:

• An ethical dilemma: When robot cars must kill, who should pick the victim?
• Should autonomous cars be allowed to speed? Results from our reader poll
• How do self-driving cars work?
• Human error as a cause of vehicle crashes
• Smart vehicles are here: Can government keep pace? | NY Times Energy for Tomorrow Conference

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

While Nate doesn’t appear in today’s comic, you can learn more about Nate’s wacky life at his Website.

A rare comic today where Nate doesn’t make an appearance.  When I first learned of the upcoming focus on automated driving, this was actually one of the first things that popped in my head.  There’s no doubt that if you saw a car coming down the highway now with no driver, that you’d absolutely freak out!  I suspect that reaction will be reversed in the future.

We are moving closer to having driverless cars on roads everywhere, and naturally, people are starting to wonder what kinds of ethical challenges driverless cars will pose. One of those challenges is choosing how a driverless car should react when faced with an unavoidable crash scenario. Indeed, that topic has been featured in many of the major media outlets of late. Surprisingly little debate, however, has addressed who should decide how a driverless car should react in those scenarios. This who question is of critical importance if we are to design cars that are trustworthy and ethical.

Driverless cars stand to reduce the number of driving fatalities overall. But there will still be fatal car crashes that are unavoidable. For example, consider this scenario in illustration, a sort of modified trolley problem (the name given by philosophers to classic examples of unavoidable crash scenarios, historically involving runaway trolleys and innocent bystanders), which I will call the tunnel problem.

Tunnel Problem: You are travelling along a single lane mountain road in an autonomous car that is fast approaching a narrow tunnel. Just before entering the tunnel a child attempts to run across the road but trips in the center of the lane, effectively blocking the entrance to the tunnel. The car has but two options: hit and kill the child, or swerve into the wall on either side of the tunnel, thus killing you. How should the car react?

Hypothetical scenarios like this one, borrowed from philosophical ethics, force us to accept the fact that some situations have only unfortunate outcomes, and focus our attention instead on the ethical complexities involved in deciding which unfortunate outcome to prefer. In these scenarios, sorting out the who question is as ethically important as sorting out the how.

If you were faced with the Tunnel Problem, what would you decide?

Take our reader poll.

In the tunnel problem, both outcomes will certainly result in harm — someone will surely die. But more importantly, from an ethical perspective there is no obvious “correct” answer to these kind of dilemmas. Tunnel problems are used as thought experiments in philosophy precisely because they are so difficult to answer, and they are popular among students for the conversations they spark at the pub.

But while philosophers in the past could ponder these kinds of difficult ethical dilemmas in the lecture hall, far removed from any practical application, when we bring autonomous cars onto the scene, these philosophical thought experiments suddenly acquire practical urgency. The tunnel problem, and trolley problems like it, point to imminent design challenges that must be addressed. From a design perspective, the tunnel problem raises the following question: How should we program autonomous cars to react in difficult ethical situations?

But there is another, related, ethical question inextricably linked to this first one: Who should decide how the car reacts in difficult ethical situations? This second question asks us to turn our attention to the users, designers, and policymakers surrounding autonomous cars, and consider who has legitimate moral authority to make decisions. We need to consider these questions together if our goal is to produce legitimate answers to either.

At first glance this second question — the who question — seems odd. Surely it is the designers’ job to program the car to react this way or that? I’d like to convince you otherwise.

I believe that in many driving situations, tunnel problems for example, it is ethically problematic to allow designers to decide how an autonomous car should react. In a draft paper I recently presented at We Robot – a conference focused on the legal, ethical, and policy issues surrounding robotics – I consider the who question in some detail. I argue that instead of assuming designers are the right people to decide in all circumstances how a driverless car should react, that we must explore design methodologies that in many circumstances allow individual drivers to decide on the preferred unfortunate outcome. My reasons stem from the nature of tunnel-like problems, and the relationship that exists between designers, drivers, and technology.

From a driver’s perspective, the Tunnel Problem is much more than a complex design issue. It is effectively an end-of-life decision. Even if we modify the problem so that serious injury is the likely outcome, Tunnel Problems pose deeply moral questions that implicate the driver directly. Seen this way, the tunnel problem is a deeply personal moral problem for the driver who happens to be in the car at that moment.

Allowing designers to pick the outcome of tunnel-like problems treats those dilemmas as if they have a “right” answer that can be selected and applied in all similar situations. In reality they do not. Is it best for the car to always hit the child? Is it best for the car always to sacrifice the driver? If we focus on designers, the tunnel problem appears to pose an impossible design problem, one that can only be solved arbitrarily.

But there is a better solution. We can look to other examples of complex moral decision making for examples of how to get some traction on the who question.

In healthcare we deal with deeply personal moral problems, such as end-of-life decisions, on a daily basis. We are so accustomed to them, in fact, that we have found ways of answering the similar who question in healthcare with some success. In medical ethics there is general agreement that it is impermissible to impose answers to deeply personal moral questions upon others, except in very special circumstances. In other words, it is generally left up to the individual for whom the question has direct moral implications to decide which outcome is preferable. When faced with a diagnosis of cancer, for example, it is up to the patient to decide whether or not to undergo chemotherapy. When faced with imminent death, it is up to the dying patient to decide if they would like CPR in the event of cardiac arrest. Doctors and nurses are trained to respect patients’ decisions, and to work around them within reason.

In healthcare, the notion of imposing medical interventions upon individuals without their express consent is so contentious that it is given a special name: paternalism. Subjecting someone to paternalism in the medical context is considered unethical, because it robs them of their personal autonomy.

An appeal to personal autonomy has intuitive appeal beyond healthcare. Why would one agree to let someone else decide on deeply personal moral questions, such as end-of-life decisions in a driving situation, that one feels capable of deciding autonomously? Why should we allow designers to decide on the outcome of tunnel problems if there is a way for individual drivers to do so autonomously?

From an ethical perspective there is nothing exceptional about the tunnel problem that suggests we should allow designers to impose an answer to that deeply personal moral question upon drivers. If we allow designers to choose how a car should react to a tunnel problem, we risk subjecting drivers to paternalism by design: cars will not respect drivers’ autonomous preferences in those situations.

What would personal autonomy look like in the context of a tunnel problem? For starters, as in healthcare we should expect drivers’ preferences to demonstrate their personal moral commitments. It could be that a very old driver would always choose to sacrifice herself to save a child. It might be that a deeply committed animal lover might opt for the wall even if it were a deer in his car’s path. It might turn out that most of us would choose not to swerve. These are all reasonable choices when faced with impossible situations. Whatever the outcome, it is in the choosing that we maintain our personal autonomy.

The upshot of considering the second question — Who should decide how the car reacts in difficult ethical situations? — is that it becomes clear that there are certain deeply personal moral questions that will arise with autonomous cars that ought to be answered by drivers. Just because designers hold the technical abilities to engineer autonomous cars does not give them the authority to impose particular moral decisions on all users. If designers assume moral authority, and subject users to paternalistic relationships, they run the risk of making technology that is less ethical, and certainly less trustworthy.

Of course, recognizing the ethical need for user input in some situations should not lead to designer paralysis. Technical and practical limitations will sometimes prevent designers from making autonomous cars that ask users to make explicit choices in all unavoidable crash scenarios. Yes, we must recognize the importance of letting drivers autonomously express certain preferences. But we must also balance the need for personal autonomy with the severity of the ethical problem posed by the design decision. Asking for driver input in all scenarios would create unreasonable barriers to design and would prevent society from realizing the many other benefits posed by the technology, such as a reduction in overall crashes. Some driver preferences will not be “serious” enough to warrant their input.

The same is true in healthcare, where sometimes consent can be assumed. Healthcare professionals are obligated to get consent from patients only in cases that warrant that consent. A proportional approach is applied to make the determination. The challenge for autonomous car designers is adopting a design methodology that allows for the identification of paternalistic design choices, and then applies a proportional assessment to determine which of those choices requires user input. Thus, just as we have proportional assessment models in healthcare, we can work to develop similar approaches when designing autonomous car technology.

None of this simplifies the design of autonomous cars. Indeed, there are significant design barriers associated with ethical issues that will need to be overcome before autonomous cars ever enter the general market. But making technology work well requires that we move beyond technical considerations in design to make it both trustworthy and ethically sound. We should work toward design methodologies that are capable of identifying paternalism, and that ultimately enable users to exercise their more of their autonomy when using technology. When robot cars must kill, there are good reasons why designers should not be the ones picking victims.

If you liked this article, you may also be interested in:

• Reader poll: If a death by an autonomous car is unavoidable, who should die?
• Should autonomous cars be allowed to speed? Results from our reader poll
• Kids with wheels: Should the unlicensed be allowed to ‘drive’ autonomous cars?
• What should a robot do? Designing robots that know right from wrong
• Morgan Stanley reports on the economic benefits of driverless cars

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Did you know that the majority of the cars we buy and drive today are able to act by themselves and maneuver themselves out of an accident? They can also beat the best human drivers in breaking accuracy and manage even the most finicky engines. Our cars may not yet be fully autonomous but they’re much closer to driving themselves than we realize.

From a pioneering mode of transport to an everyday necessity (or necessary evil) for commuters the world over, the car has come a long, long way since its inception. In its early days, the act of driving required significant expertise; now it is considered almost a right, directly linked with a person’s mobility or even personal freedom. The trend towards  automation covers all aspects of a car’s operation and systems, including driving aids such as climate control and smart lighting, as well as control of the actual movement of the vehicle. This article gives an overview of the milestones along the road to autonomy, focusing especially on the systems and devices that directly control the behaviour of the car: the engine and transmission, the steering and the breaks, and the various sensors and processors that provide and manage the data.

Drivers of the first passenger cars were fully occupied with engine management, and had to control the ignition timing and the air-fuel mix, and perform a highly synchronized ballet of movements just to change gear. Gradually, however, the engine and transmission of our cars became more and more self-sufficient. Manual ignition control gave way to mechanical and then to electronic control. Sensitive carburetors were replaced by self-controlled electronic fuel injection systems, while manual transmissions evolved and became much easier to use. Early on, automatic transmission systems were available to the public where the economy and the market allowed. Nowadays efficient multi-gear automatic transmissions are exceedingly popular and are rapidly replacing manual gearboxes. And of course there is a substantial and growing percentage of electric cars, which can be operated almost as easily as a child’s toy.

The important thing is that, in many commercially available cars today, the input from the driver towards the engine and transmission passes through potentiometers and actuators, with no mechanical link. A computer filters the input from the driver and manages the engine and transmission accordingly.

Data input for the engine management is provided by the engine monitoring sensors, but also from the ESP/ABS (more on that below) and even from the navigation system. By using GPS and cellphone coverage, a car may predict its route and act accordingly, for example changing gear before a steep slope, or having the break pressure stand by in maximum pressure before a bend.

Steering has been more resistant to technological advances; the steering in a car today isn’t very different in principle than that of a 1910 Ford Model T. The most significant improvement was the addition of hydraulic, and later, electric (or electro-hydraulic) assistance. The driver has always a direct link with the steering wheels and, apart from active steering systems (mentioned below), few advances have been made in this area.

It probably isn’t a coincidence that brakes, arguably the most sensitive area from a safety point of view, are by far the most automated system of an everyday car, and where people are less reluctant to have their actions moderated by computers. While, as mentioned above, loss of direct mechanical linkage is relatively new to throttle (usually this loss is found only in research vehicles for steering), brakes lost their mechanical linkage almost a century ago. Their hydraulic system (used in practically all passenger cars) not only augments the force of the driver but, aided by a system of valves, distributes the breaking force in order to compensate for weight transfer.

The biggest breakthrough though was the introduction of anti-lock breaking system (ABS) first introduced in the 60s. It was designed to prevent the wheels from locking up while the vehicle was moving. Its operation is relatively simple: speed sensors on each wheel detect lock and electronically controlled valves reduce breaking force on the each wheel, thus keeping it within the desired slip percentage (~20% or less). Although a very good driver could use threshold breaking in order to maintain traction, ABS is a milestone because it can perform better than any human by monitoring and controlling the breaking force on each specific wheel. One would have to have four fast, super-human feet and four break pedals (along with the necessary brain capacity) to perform as well as an ABS system, which is found in almost any car on the market today, even the cheapest ones.

An even bigger breakthrough that quietly saved many lives and averted numerous accidents is what’s known as electronic stability program, or ESP. It’s an evolution of ABS and uses wheel speed sensors, a steering wheel position sensor, a simple (by robotic standards) yaw sensor and algorithms to govern the ABS valve unit. Judging from each wheel speed, the steering angle and the yaw of the car, the ESP monitors the car’s trajectory (as dictated by the amount the driver turns the steering wheel). If it detects any deviations, it breaks individual wheels in order to pivot the car towards the desired direction. It can also take into account the throttle travel and reduce the power of the car if necessary.

The result is a system that monitors the driver’s input and acts directly through the breaks (and throttle) to retain control of the car. By detecting skids at their earliest stages, and acting quickly on individual wheels and the engine, it can prevent accidents more effectively than any human could.
(You can also check this older video of TV show’s Fifth Gear that demonstrates the abilities of ESP.)

Some new systems also employ steering to assist the driver in controlling the car, such as actively controlled rear-wheel steering – a system occasionally used in specific high performance cars for more than two decades, or as it is the case more recently, by acting directly on the main steering system of the front wheels. By adding a corrective input, ESP can use steering to introduce small corrections along with any breaking action.

Active steering is not yet very wide-spread, although rear wheel steering under computer control is quite old (Honda, Nissan, BMW and Mitsubishi were selling cars with active 4WS decades ago, Porsche and Renault still are today). Active front wheel steering is newer but still rare. It’s worth mentioning that it always acts as an additional input and the driver maintains a direct mechanical linkage to the steering. The ESP control mechanism adds an additional or counterweight force (usually through a planetary gear, in parallel with the steering column) that translates to a slightly corrected slip angle.

A different approach with similar (thought not identical) results is to employ active differentials. A system with clutches and/or auxiliary gearboxes inside the transmission and differentials can actively manage how much power is delivered on each wheel, thus providing the necessary yaw moment to influence the attitude of the car. The effect is similar to active rear wheel steering, providing yaw moment without slowing the car (like individual wheel breaking does). This is why, like RWS, active differentials are mostly used on performance cars.

Another area of gradual automation is adaptive cruise control systems. This is an enhanced version of the regular cruise control systems that are very popular in the US (although not so much in Europe). Cruise control maintains a certain speed until the driver hits the breaks. Additionally, adaptive cruise control can automatically reduce speed when the car is approaching a vehicle in front of it and restate the desired speed when conditions allow.

The system usually includes a radar sensor placed in the front bumper and the same technology is also applied in collision avoidance systems even without the cruise control function. By detecting obstacles in front of it, the car can notify the driver or even take action and break by itself. Nowadays even very small and inexpensive cars can be equipped with it, and its performance is more than adequate. A similar function is the lane departure warning, where sensors (usually placed under the rear-view mirrors) scan the road and detect from the white lines if the vehicle is going off course.

Finally the most impressive task a commercial passenger car can do today is without doubt park by itself. Already on sale, park-assist can scan the surroundings and perform the necessary maneuvers in order to park, as long there is sufficient space. Probably a luxury and not really necessary given that it operates where even a mediocre driver can easily park, it’s is the most visible picture of a self-driven car available on the market.

A very important point is that all the systems described above could co-operate with each other and maximize their influence. Although in general they’re used independently it is inevitable that they’ll be integrated into a complete set. Steps towards this direction have already been made but there is a lot of room for improvement.

How close are today’s cars to complete automation?

Engine-wise, many cars today (and certainly more in the future) have no mechanical link between the throttle pedal and the engine. Power delivery is under fully electronic control, so from the perspective of the engine, everything is primed for a fully autonomous car.

Likewise for the powertrain and transmission. Automatic gearboxes are ever more popular and already they require no human input. Of course things are even easier for electric cars.

Breaks are also already highly automated, but they usually rely on the force one is exerting on the pedal. However by-wire breaks have already been used in passenger cars (Mercedes W211 & R230) even with an analog hydraulic backup for safety (or legislative reasons).

The steering system, although it will probably be the last to come fully under computer control, is already getting there with active steering systems currently on sale. The step from an assistive active steering system to a full authority one is very small (at least technically).

The data input and processing necessary for an autonomous car is technically the most difficult part by far. What we described above is automation that builds on human input: even the most capable commercial cars today rely on their drivers to tell them where to go, how fast and what to avoid. A new set of sensors along with the necessary onboard computational power should eventually substitute for the driver, but this is already happening both partially (as we examined in this post) and fully, as in Google’s new purpose-built self-driving car.

Though functional fully autonomous cars exist and perform well even today, the last obstacle they have to overcome in order to reach the market is the product liability and the legal complications of having a car acting on its own.

Glossary:

VGS: variable gear ratio steering

If you liked this article, you may also be interested in:

• How do self-driving cars work?
• Pure autonomy: Google’s new purpose-built self driving car
• SAE defines six levels of driving automation
• Human error as a cause of vehicle crashes

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Tesla CEO, Elon Musk, recently announced that the car manufacturer will produce self-driving cars within three years. Nissan has announced that it will have a self-driving car available by 2020, Google has said it will do so by 2018. Over the past decade, the conversation around self-driving cars has evolved from futuristic police chase sequences in Minority Report to figuring out which auto manufacturer will be first to launch a commercially viable self-driving vehicle. Daimler AG, maker of Mercedes Benz, recently announced that an S-class sedan had completed a 62-mile journey in the streets of Germany without a driver. Audi’s self-driving car successfully navigated 156 turns of the 12-mile Hill Climb course in Colorado’s Pikes Peak. Car manufacturers see self-driving cars as a way to eliminate road deaths caused by human error, reduce traffic, and free up time spent commuting – but how do these vehicles work?

Self-driving cars in a nutshell

A self-driving car is capable of sensing its environment and navigating without human input. To accomplish this task, each vehicle is usually outfitted with a GPS unit, an inertial navigation system, and a range of sensors including laser rangefinders, radar, and video.  The vehicle uses positional information from the GPS and inertial navigation system to localize itself and sensor data to refine its position estimate as well as to build a three-dimensional image of its environment.

Data from each sensor is filtered to remove noise and often fused with other data sources to augment the original image. How the vehicle subsequently uses this data to make navigation decisions is determined by its control system.

The majority of self-driving vehicle control systems implement a deliberative architecture, meaning that they are capable of making intelligent decisions by 1) maintaining an internal map of their world and 2) using that map to find an optimal path to their destination that avoids obstacles (e.g. road structures, pedestrians and other vehicles) from a set of possible paths. Once the vehicle determines the best path to take, the decision is dissected into commands, which are fed to the vehicle’s actuators. These actuators control the vehicle’s steering, braking and throttle.

This process of localization, mapping, obstacle avoidance and path planning is repeated multiple times each second on powerful on-board processors until the vehicle reaches its destination.

The next section focuses on the technical components of each process: mapping and localization, obstacle avoidance and path planning. Although car manufacturers use different sensor suites and algorithms depending on their unique cost and operational constraints, the processes across vehicles are similar. The descriptions below most closely mirror their implementation in state-of-the-art self-driving military vehicles.

Breaking Down the Technicals

Mapping and Localization

Prior to making any navigation decisions, the vehicle must first build a map of its environment and precisely localize itself within that map. The most frequently used sensors for map building are laser rangefinders and cameras. A laser rangefinder scans the environment using swaths of laser beams and calculates the distance to nearby objects by measuring the time it takes for each laser beam to travel to the object and back. Where video from camera is ideal for extracting scene color, an advantage of laser rangefinders is that depth information is readily available to the vehicle for building a three-dimensional map. Because laser beams diverge as they travel through space, it is difficult to obtain accurate distance readings greater than 100m away using most state-of-the-art laser rangefinders, which limits the amount of reliable data that can be captured in the map. The vehicle filters and discretizes data collected from each sensor and often aggregates the information to create a comprehensive map, which can then be used for path planning.

For the vehicle to know where it is in relation to other objects in the map, it must use its GPS, inertial navigation unit, and sensors to precisely localize itself. GPS estimates can be off by many meters due to signal delays caused by changes in the atmosphere and reflections off of buildings and surrounding terrain, and inertial navigation units accumulate position errors overtime. Therefore localization algorithms will often incorporate map or sensor data previously collected from the same location to reduce uncertainty. As the vehicle moves, new positional information and sensor data are used to update the vehicle’s internal map.

Obstacle Avoidance

A vehicle’s internal map includes the current and predicted location of all static (e.g. buildings, traffic lights, stop signs) and moving (e.g. other vehicles and pedestrians) obstacles in its vicinity. Obstacles are categorized depending on how well they match up with a library of pre-determined shape and motion descriptors. The vehicle uses a probabilistic model to track the predicted future path of moving objects based on its shape and prior trajectory. For example, if a two-wheeled object is traveling at 40 mph versus 10 mph, it is most likely a motorcycle and not a bicycle and will get categorized as such by the vehicle. This process allows the vehicle to make more intelligent decisions when approaching crosswalks or busy intersections. The previous, current and predicted future locations of all obstacles in the vehicle’s vicinity are incorporated into its internal map, which the vehicle then uses to plan its path.

Path Planning

The goal of path planning is to use the information captured in the vehicle’s map to safely direct the vehicle to its destination while avoiding obstacles and following the rules of the road. Although manufacturers’ planning algorithms will be different based on their navigation objectives and sensors used, the following describes a general path planning algorithm which has been used on military ground vehicles.

This algorithm determines a rough long-range plan for the vehicle to follow while continuously refining a short-range plan (e.g. change lanes, drive forward 10m, turn right). It starts from a set of short-range paths that the vehicle would be dynamically capable of completing given its speed, direction and angular position, and removes all those that would either cross an obstacle or come too close to the predicted path of a moving one.  For example, a vehicle traveling at 50 mph would not be able to safely complete a right turn 5 meters ahead, therefore that path would be eliminated from the feasible set.  Remaining paths are evaluated based on safety, speed, and any time requirements. Once the best path has been identified, a set of throttle, brake and steering commands, are passed on to the vehicle’s on-board processors and actuators. Altogether, this process takes on average 50ms, although it can be longer or shorter depending on the amount of collected data, available processing power, and complexity of the path planning algorithm.

The process of localization, mapping, obstacle detection, and path planning is repeated until the vehicle reaches its destination.

The Road Ahead

Car manufacturers have made significant advances in the past decade towards making self-driving cars a reality; however, there still remain a number of technological barriers that manufacturers must overcome before self-driving vehicles are safe enough for road use. GPS can be unreliable, computer vision systems have limitations to understanding road scenes, and variable weather conditions can adversely affect the ability of on-board processors to adequately identify or track moving objects. Self-driving vehicles have also yet to demonstrate the same capability as human drivers in understanding and navigating unstructured environments such as construction zones and accident areas.

These barriers though are not insurmountable. The amount of road and traffic data available to these vehicles is increasing, newer range sensors are capturing more data, and the algorithms for interpreting road scenes are evolving. The transition from human-operated vehicles to fully self-driving cars will be gradual, with vehicles at first performing only a subset of driving tasks such as parking and driving in stop-and-go traffic autonomously. As the technology improves, more driving tasks can be reliably outsourced to the vehicle.

The technology for self-driving cars is not quite ready, but I am looking forward to the self-driving cars of Minority Report becoming a reality.

If you liked this article, you may also be interested in:

• Pure autonomy: Google’s new purpose-built self driving car
• Morgan Stanley reports on the economic benefits of driverless cars
• SAE defines six levels of driving automation
• Focus Series on Big Deals: What It Means to Have the Giants Investing in Robotics
• Focus Series: The state-of-the-art and the future of agricultural robotics

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

Learn more about Nate’s wacky life at his Website.

Google completed a major step in its long and extensive self-driving cars project by presenting its first purpose-built autonomous car, which is designed from scratch for its role and is not a modified conventional Toyota.

The as yet unnamed car is very small (looks smaller than a Smart) and can accommodate two people and some luggage. It’s probably electric and its maximum speed is limited to 25mph (~40km/h). Its most striking characteristic is that it doesn’t have any controls — no steering wheel, accelerator or brake pedals — and you can ride it strictly as a passenger, which is probably a strange feeling, but according to Google’s video not entirely unpleasant.

This is a purely experimental project in every aspect. Google will make about a hundred prototypes and it looks like it’s a very serious effort. It is equipped with multiple sensors (a big LIDAR on its roof is probably doing most of the work), but there are also sensors in the front, in the back and where the side view mirrors would have been in a regular car. Thanks to Google’s previous experience with self-driving cars, one can expect very good performance in real environments; other Google self-driving cars have completed hundreds of thousands miles with no major incidents.

The car itself may look like a toy but it is cleverly and purposefully designed to be as cute as possible in order to inspire trust and reduce fear of its autonomous status. Small cars in general may be more vulnerable in a crash with a heavier vehicle, but it’s already proven that with clever engineering you can have almost no compromises in their passive safety, and most small cars from established manufacturers achieve very high scores in a crash test. The main chassis is made from robust box sections of (probably) aluminum tubing and there’s also a tubular roll cage above. Even the suspension wishbones look highly over-engineered for a 25mph city car. A substantial crash box is placed in the front, and above it the whole front panel is made of foam, so the car is not only safe for its passengers but for pedestrians as well. Of course at this stage there are no independent crash tests to verify its performance.

As mentioned above, the most striking aspect of the car is its lack of any kind of manual control. This is a very clever move from Google that aspires to overcome the legal and ethical problems of who should be able to drive or control a self-driving car.

If you completely eliminate any kind of input from the occupants (apart from the destination selection), then anyone on board is defined strictly as a passenger and a question like “should we allow a child to ‘drive’ an autonomous car” is transformed into “should we allow a child to be transported by an autonomous car” — a question that is still probably not very easy to answer, but which is certainly easier than the former one.

Google will launch a small pilot program in California in the next couple of years. The prototypes released on public roads will have manual controls for obvious legal reasons but the goal of this project is to learn and develop the technology and know-how. Read the full post in Google’s official blog here.

It’s worth mentioning that similar concepts and ideas (but not a functional prototype) have been presented over the years. The concept closest to this is ‘UC’ by the pioneering design firm Rinspeed, presented four years ago at the Geneva Motor Show. It was also a two-seater (loosely based on a Fiat 500) but its main feature was the interior, where similarly to Google’s car, no manual controls were present.

Reader poll results: Earlier this month, when we asked people about your general thoughts on autonomous cars, we found that one of the main advantages of autonomous cars is that those who are not licensed to drive will be able to get to places more conveniently. This led us to wonder more about who should be able to drive an autonomous car.

We asked three questions through Robohub to find out more. Here’s what you, dear readers, have said.

Should a child under the legal driving age be allowed to ride driverless cars alone?

Remember the first time your parents handed you their car keys, or took you out for your very first driving lesson? For some of us, that moment came with our parents beaming aloud expressions of uncertainty, anxiety, and worry. Some of this adult/parental psyche may be at play with this question.

About half of the participants (52%) said that children under the legal driving age should not be able to ride driverless cars. This is not entirely surprising. Currently, under driving regulations, children under a certain age (typically late teen years) are not allowed to drive cars because we have reasons to believe that it is not entirely safe to let them drive. By allowing a child to ride autonomous cars alone, people may be concerned about scenarios where the car makes an error or requires the assistance of a human decision-maker during an emergency situation. Interestingly, 38% of the participants believe that children should be able to ride driverless cars alone and the other 10% also think that children should be able to drive autonomous cars with proven technology and specific training. This brings us to a fairly even split in opinion on the issue of children driving autonomous vehicles.

The results may not mean that 48% of the participants would be comfortable putting a newborn baby in an autonomous car to be delivered to his/her grandparents’ house. But it could mean that with a proven technology and some training, the legal driving age could be lowered for ‘driving’ autonomous cars. It makes sense that a shift in driving age would depend on the capability and reliability of the technology. For example, if all cars today had a high probability of breaking down in the middle of the road, then our driver’s license exams may have included a preliminary car mechanics test.

For now, it seems that people are more hesitant to put children in autonomous cars alone. It would be interesting to see if this will change as the technology drives out onto the road and demonstrates its safety and reliability.

Should a senior who no longer has a legal driving license be allowed to ride driverless cars alone?

Compared to the case of the underage drivers, it seems that the majority of the participants (67%) consider it acceptable to have a senior with no legal driver’s license ride an autonomous vehicle alone. Perhaps our participants expect a senior who has had a driver’s license to be able to better handle any emergency or unexpected situations than children, since they have driving experience to rely on if necessary. However, it is possible that some of the seniors are without license due to low cognitive and/or physical capabilities. In such cases, the concerns over the rider being able to handle emergency/unexpected situations is not different from the above unlicensed child rider situation.

This may be one of the reasons why 26% of the participants are not in support of unlicensed seniors riding the autonomous cars alone. However, similar to the responses to the first question, 7% of the participants thought that they would answer yes to this question only if there are limitations placed on where the senior would be allowed to ride the vehicle and whether they would have to pass some type of training to ride the vehicle.

Should a legally blind person be allowed to ride driverless cars alone?

Interestingly, the split of the votes for this question lies somewhere between the above two questions. 58% of the participants answered “yes” while 37% said “no” and the remaining 5% answered with a conditional “yes”. So why is a significant portion of the responses affirmative considering that being able to see is one of the most essential functions we rely on when we drive?

Perhaps people expect the legally blind driver to be able to receive enough information and be able to make informed driving decisions to handle emergency or unexpected situations. It maybe that people are in support of the blind driver scenario because the benefits of driving outweigh the risks for legally blind persons, more so than for a child or a senior. Compared to the case of the underage driver, a legally blind person (assumed adult) is deemed to have better decision making abilities and able to take responsibilities, which are both important requirements for a driver besides being able to perceive the environment.

Drawing a line on who should and should not be able to ride an autonomous vehicle seems to depend on a number of factors, and that doesn’t really include whether the individuals are legally licensed to drive under today’s regulations. Based on the results, the capability and reliability of the autonomous vehicles and the cognitive and physical ability of the rider are likely to change the way we allow different individuals to ‘drive’. One thing is for certain: regulations on who can drive will have to be reconsidered once autonomous vehicles start roaming the streets.

Share your opinion and take our next reader poll: Should autonomous cars be allowed to speed?

The results of the survey presented in this post have been analyzed and written by AJung Moon, Camilla Bassani, Shalaleh Rismani and Fausto Ferreira at the Open Roboethics initiative.

If you liked this article, you may also be interested in:

• What do people think about autonomous cars? Results from our reader poll
• What should a robot do? Designing robots that know right from wrong
• Morgan Stanley reports on the economic benefits of driverless cars
• The autonomous driving r/evolution: Call to contribute to Robohub Focus

See all the latest robotics news on Robohub, or sign up for our weekly newsletter.

While a city full of autonomous taxis may seem part of a distant future, sophisticated driver-assistance systems such as ABS, cruise-control, lane-keeping and self-parking are already a reality in today’s auto market. 
Recently the US Department of Transportation announced their decision to move forward with vehicle-to-vehicle communication technology to allow cars to exchange data with each other to improve road safety, suggesting that the autonomous car (r)evolution is indeed well upon us.

What technical advances are bringing autonomous cars and highway systems closer to reality? How do autonomous vehicles experts envision the future of personal transportation? What are the market, legal and policy forces at play?

Robohub is planning a focus series on the state-of-the-art and the future of autonomous driving, and we are inviting readers with expertise in the field to contribute.

Suggested Topics

What do you think are the main advantages of owning an autonomous car?

Perhaps this is unsurprising, but “Less traffic accidents!” seems to be the general consensus here. Over eighty percent of all respondents (83%) said that one of the main advantages of autonomous cars is reduction of the number of traffic accidents. The next set of popular answers were that autonomous cars will free up time and energy spent on driving (77%), allow those who are not licensed to drive will be able to get to places more conveniently (71%), and that it would reduce traffic congestion (67%). One point that surprised at least some of us was that only 40% of the respondents consider improvements to taxi and car-sharing services as a main advantage.

This may be surprising because, some of us current users of car-sharing services or frequent customers of taxi-cabs could easily imagine the improvements autonomous cars could have (e.g., no need to park shared cars at specific locations). But it is likely that more people have their own vehicles and may not consider this improvement as one of the main advantages. We also received a wide range of interesting responses from those who told us about ‘other’ advantages of the technology (11%). The responses range from “it will be cool!” – yes, we agree – to specific statements about fuel efficient driving, being able to ride a car while intoxicated/texting/eating/working, and the potential to improve delivery services and reducing shipping and delivery costs.

What is the maximum amount (USD) you would pay extra for a fully autonomous car?

Given that most people chose at least one advantage to autonomous cars, it was interesting to find out how much people were willing to pay for the technology. When asked about how much they would be willing to pay for a fully autonomous car, 66% of the respondents said that they would pay over $3000 (USD) in addition to what a conventional version of the car would cost. Only 5% said that they would never buy an autonomous car, while 12% said that they would buy the car regardless of the price. This is actually a very different result compared to a study conducted by Accenture Research in 2010, in which they reported that 51% of people would not feel comfortable using an autonomous car. It’s possible that people’s attitude about autonomous cars have changed over the 4 year period, or that our survey attracted more people from the technology industry given that majority of Robohub’s readers are technology enthusiasts or working in technology sector.

Should an autonomous car be easily identifiable?

According to our survey, 61% of people think that autonomous cars should be easily identifiable, similar to taxicabs and police cars. This made us raise questions about why most people would want to identify autonomous cars. Is it for having the bragging rights to show off what a cool car you drive? Is it because people want to be extra careful, or maybe less careful(?), when driving within the vicinity of an autonomous car? Or is it that traffic policing could be a nightmare somehow if police officers couldn’t tell apart autonomous cars from conventional cars? Maybe people think that it doesn’t hurt to make them distinguishable, whereas not being able to distinguish them from conventional cars might have negative implications.

Over the two-week period, we collected a total of 114 responses, which we think is amazing given that this is the very first poll of this sort ORi has worked on. If you didn’t get to participate in the poll this time around, don’t be disappointed. We will be releasing our reader polls every other Tuesday on Robohub (yes, that includes tomorrow), so you can check back and let us know what you think.

The results of the poll presented in this post have been analyzed and written by AJung Moon, Camilla Bassani, Shalaleh Rismani and Fausto Ferreira at the Open Roboethics initiative.