Source: Ford Velodyne LiDAR/YouTube

In anticipation of the need for LiDAR devices in cars with assisted steering and other self-driving technologies, both Velodyne and Quanergy received funding. Quanergy raised $90 million and Velodyne got $150 million.

Source: Frost & Sullivan;VDS Automotive SYS Konferenz 2014/

As can be seen in the chart provided by Frost & Sullivan, LiDARs will play a big role in all phases of the emergence of self-driving vehicles. Hence the immediacy of these two investments in LiDAR technology.

Velodyne LiDAR, a Morgan Hill, Calif.-based provider of technology that allows self-driving cars see and avoid what’s around them, has raised $150 million in equity funding from Ford Motor Co. and Baidu. Velodyne expects an exponential increase in LiDAR sensor deployments in autonomous vehicles and ADAS applications over the next several years, driving high revenue growth. The funds will be used to help Velodyne lower the cost of their LiDAR to between $300 and $500, the price point at which mass adoption becomes practical.

“LiDAR continues to prove itself as the critical sensor for safe autonomous vehicle operation,” said David Hall, founder and CEO, Velodyne LiDAR. “This investment will accelerate the cost reduction and scaling of Velodyne’s industry-leading LiDAR sensors, making them widely accessible and enabling mass deployment of fully autonomous vehicles. We are determined to help improve the goal of safety for automotive vehicles as soon as possible, as well as empower the efficiency autonomous systems offer.”

“We want the cost to be low enough to be used for all cars. We envision a safer world for the millions of automotive drivers across the globe,” said Marta Hall, Velodyne President of Business Development.

Quanergy Systems Inc., a Sunnyvale, Calif.-based provider of solid state LiDAR sensors and smart sensing solutions, has raised $90 million. Backers include Sensata Technologies, Delphi Automotive, Samsung Ventures, Motus Ventures and GP Capital. Quanergy’s solid state LiDAR is included in the frontal piece of the Mercedes test car shown on the right. The solid-state nature of the LiDAR is a significant cost savings and the expected trade price (once full production begins) will be less than $250.

LiDARs aren’t the only components getting funded

In a NY Times story, Delphi and Mobileye said that they had formed a partnership to provide a core self-driving platform to automakers by the end of 2019.

The Mobileye-Delphi collaboration “will accelerate the time to market” and enable carmakers to produce vehicles capable of driving themselves “without the need for huge capital investments,” said Amnon Shashua, chairman and chief technology officer of Mobileye.

Delphi, the former parts division of General Motors, and Mobileye, an Israeli company known for producing the sensors and other technology that help cars recognize obstacles in the road, are both major component providers to the auto industry.

Image credit: Delphi

Delphi Automotive, the global provider of vehicle electronics components, will begin testing 6 phone-dispatched autonomous taxis in Singapore that will go point-to-point based on customer requests with a goal of providing no-driver-in-the-car service by 2019, and cars without steering wheels by 2022.

Delphi Automotive already provides some of the autonomous systems that are beginning to appear in new cars, as seen by this components’ graphic.

Image source: EDN Network

Delphi has a small autonomous fleet in Silicon Valley and last year demonstrated a coast-to-coast autonomous drive.

According to Glen DeVos, a Delphi senior vice president, at a briefing in Troy, MI:

“We actually will have point-to-point automated mobility on demand with no driver in the car. It’s one of the first, if not the very first, pilot programs where we’ll demonstrate mobility-on-demand systems.

Singapore is a small island and right now, for the individual to get to the mass-transit systems, it’s not easy. The city is really trying to lead the world in addressing urban congestion.

Singapore’s Land Transport Authority chose Delphi for the test as the city looks to driverless vehicles to address growing gridlock. It asked Delphi to provide robot rides to get commuters to mass-transit stations so fewer cabs will be clogging the roads.”

Image credit: Delphi

[Actually, regarding DeVos’ claim that they are the first, NuTonomy, a spinoff from MIT, has plans to deploy “thousands” of fully self-driving taxis all over Singapore by 2019. The company is already testing its vehicles in an area of Singapore’s business district called 1 North. NuTonomy will expand to a nearly four-mile radius as early as this summer.]

DeVos said Delphi plans to announce similar pilot programs in the U.S. and Europe very soon. If all goes as planned, the next phase will be deploying fully autonomous cars without steering wheels by 2022, he said.

Google’s self driving car. Source: Google

The press were all a-twitter about a report from Reuters that there had been a near miss between Delphi’s test car and one of Google’s, though it was quickly denied that anything happened.

The situation that was described — one car cutting off another — was a very unlikely one for several reasons:

• All these cars are operated by trained safety drivers who are expected to be vigilant and take control at any sign of trouble.
• In particular, special moves like a lane change would get extra vigilance. If something unusual happened (such as two cars going for the same spot) the safety drivers would be watching in advance, tracking what the car was doing, and pulling back if the car’s own displays were not telling them it was going to do the right thing.

The safety drivers are not perfect of course, but an autonomous lane change is a rare event and one that most people are still just testing, so they would be very unlikely to miss that the car was going to cut somebody else off.

Of course, situations will arise when two cars try to change into the same spot at the same time, and robocars will probably be fairly timid in these situations. The most likely situation if two robocars tried to take the same spot would be that both would back off and return to their original lane, and it will probably be that way until being so timid is not a workable strategy.

Robocars won’t be the lane-changing demons that some people (including myself) sometimes are. Many human drivers are constantly trying to find the fastest lane and we weave, only to find that the lane we just moved into has become the slowest. Part of that is our psychology.

Robocars won’t do this as much because their passengers will be occupied doing other things, and in most cases will not be in a super hurry. Those passengers will prefer a stable ride, where they can get work done, to a weaving ride with extra starts and stops. If we’re in a big hurry, we might ask the car to try to work extra hard to make the fastest trip but this will be the exception.

When we do want that, the robocar will actually have a very nice model of just how fast each lane is moving. It won’t be fooled the way we are by seeing some lanes that seem to be faster when in fact neither lane is winning by that much. If they read licence plates to identify cars, they will get excellent appraisals of what’s going on. If one lane is truly faster they will find it. On the other hand, they will be worse at the standard game of chicken needed to change lanes in heavy traffic, where you depend on the car you are moving in front of to slow down. They will know the physics though, and if a lane change is needed, they will warn the passengers of high acceleration and perfectly make a smaller spot than you might be able to make.

In other news, Google has sent two cars to Austin, Texas to expand their testing ground. I don’t have a particular insight on why they selected Austin — but I know that many towns and states regularly contact Google in the hope they might bring some cars to their area, though Texas has no modified laws yet.

Vislab

I’ve written a few times about the work of Vislab in Parma, Italy. They have a focus on doing self-driving with machine vision, and did a famous cross-continent trek from Italy to Shanghai a few years ago, using a lead car to map the way and a following car self-driving, mostly with vision.

This lab was spun out of its university but now has been acquired by Ambarella, a company that specializes in video compression chips, for $30M. One can see why Ambarella would want a computer vision lab — but it seems this might spell the end of their self-driving efforts, unless they are spun out.

Emissions

A new paper is out in Nature Climate Change on the potential for robocars to reduce emissions, in spired by some of my research in this area. Sadly, it’s behind a paywall, but the author will give a talk at Nissan’s lab in Silicon Valley on July 15th at our local self-driving car meetup.

The team reported that the vehicle drove 99% of the time. This is both an impressive and unimpressive number, and understanding that is key to understanding the difficulty of the robocar problem.

One of the earliest pioneers, Ernst Dickmanns did a long highway drive 20 years ago, in 1995. He reported the system drove 95% of the time, kicking out every 10km or so. This system simply found the edge of the road, and kept in the lane by tracking that. Delphi’s car is much more sophisticated, with a very impressive array of sensors — 10 radars, 6 lidars and more, and it has much more sophisticated software.

99% is not 4% better than 95%, it’s 5 times better, because the real number is the fraction of road it could not drive. And from 99%, we need to get something like 10,000 times better — to 99.9999% of the time, to even start talking about a real full-auto robocar. In the US we drive 3 trillion miles per year, and are on the road about 60 billion hours, a little over half of that time on the highway. 99.9999% for all cars would mean still too many accidents if 1 time in 10 million you encountered something and could not handle it.

However, this depends on what we mean by “being unable to handle it.”

• If not handling means “has a fatal accident” that could map to 3,600,000 of those, which would be 100x the human rate and not acceptable.
• If not handling it means “has any sort of accident” then we’re pretty good, about 1/4th of the rate of human accidents.
• If not handling it means that the vehicle knows certain roads are a problem, and diverts around them or requests human assistance, it’s no big problem at all.
• Likewise if not handling it means identifying a trouble situation, and slowing down and pulling off the road, or even just plain stopping in the middle of the road — which is not perfectly safe but not ultra-dangerous either — it’s also not a problem.

At the same time, our technology is an exponential one, so it’s wrong to think that the statement that it needs to be 10,000 times better means the system is only 1/10,000th of the way there. In fact, getting to the goal may not be that far away, and Google is much further along. They reported a distance of over 80,000 miles between necessary interventions. Humans have accidents about ever 250,000 miles.

It should also be noted that Delphi’s result is almost entirely on highways, which are the simplest roads to drive for a robot. Google’s result is also heavily highway biased, though they have reported a lot more surface street work. None of the teams have testing records in complex and chaotic streets such as those found in the developing world, or harsh weather.

It is these facts that lead some people to conclude this technology is decades away. That would be the right conclusion if you were unaware of the exponential curve the technologies and the software development are on.

Huge Hyundai investment

For some time, I’ve been asking where the Koreans are on self-driving cars. Major projects arose in many major car companies, with the Germans in the lead, and then the US and Japan. Korea was not to be seen.

Hyundai announced they would produce highway cruise cars shortly (like other makers) but they also announced they would produce a much more autonomous car by 2020 — a similar number to most car makers as well. Remarkable though was the statement that they would invest over $70 billion in the next 4 years on what they are calling “smart cars,” including hiring over 7,000 people to work on them. While this number includes the factories they plan to build, and refers to many technologies beyond robocars, it’s still an immense number. The Koreans have arrived.

This post originally appeared on robocars.com.

Delphi has made waves by undertaking a road trip from San Francisco to New York in their test car, which is equipped with an impressive array of sensors. The trip is now underway, and on their page you can see lots of videos of the vehicle along the trek.

The Delphi vehicle is one of the most sensor-laden vehicles out there, and that’s good. In spite of all those who make the rather odd claim that they want to build robocars with fewer sensors, Moore’s Law and other principles teach us that the right procedure is to throw everything you can at the problem today, because those sensors will be cheap when it comes time to actually ship — especially for those who say they won’t ship for a decade.

At the same time, according to Kristen Kinley at Delphi, their test involves mostly highway driving, with very minimal urban street driving. They are attempting off-map driving, which is possible on highways due to their much simpler environment. Like all testing projects these days, there are safety drivers in the cars ready to intervene at the first sign of a problem.

Delphi is doing a small amount of dedicated short-range communications (DSRC) vehicle-to-infrastructure testing as well, though this is only done in Mountain View where they used some specially installed roadside radio infrastructure equipment.

Delphi is doing the right thing here — getting lots of miles and different roads under their belt. This is Google’s giant advantage today. Based on Google’s announcements, they have more than a million miles of testing in the can, and that makes a big difference.

Hype and reality of Tesla’s autopilot announcement

Telsa has announced they will do an over-the-air upgrade of car software in a few months to add autopilot functionality to existing models that have sufficient sensors. This autopilot is the “supervised” class of self driving that I warned may end up being seen as boring. The press have treated this as something immense, but as far as I can tell, this is similar to products built by Mercedes, BMW, Audi and several other companies, and even sold in the market (at least for traffic jams) for a couple of years now.

The other products have shied away from doing full highway speed in commercial products, though rumours exist of it being available in commercial cars in Europe. What is special about Tesla’s offering is that it will be the first car sold in the US to do this at highway speed, and they may offer supervised lane change as well. It’s also interesting that, since they have been planning this for a while, it will come as a software upgrade to people who bought their technology package earlier.

UK project budget rises to £100 million

What started with a £10 million pound prize in the UK has grown become over 100m in grants in the latest UK budget. While government research labs will not provide us with the final solutions, this money will probably create some very useful tools and results for the private players to exploit.

MobilEye releases their EyeQ4 chip

MobilEye from Jerusalem is probably the leader in automotive machine vision, and their new generation chip has been launched, but won’t show up in cars for a few years. It’s an ASIC packed with hardware and processor cores aimed at doing easy machine vision. My personal judgement is that this is not sufficient for robocar driving, but MobilEye wants to prove me wrong. (The EQ4 chip does have software to do sensor fusion with LIDAR and Radar, so they don’t want to prove me entirely wrong.) Even if they are not good enough on their own, ME chips offer a good alternate path for redundancy.

Chris Urmson gives a TED talk about the Google Car

Talks by Google’s team are rare — the project is unusual in trying to play down its publicity. I was not at TED, but reports from there suggest Chris did not reveal a great deal new, other than repeating his goal of having the cars be in practical service before his son turns 16. Of course, humans will be driving for a long time after robocars start becoming common on the roads, but it is true that we will eventually see teens who would have gotten a licence never get around to getting one. (Teens are already waiting longer to get their licences, so this is not a hard prediction.)

The war between DSRC and more wifi is heating up.

Two years ago, the FCC warned that since auto makers had not figured out how to benefit from the DSRC spectrum at 5.9ghz, it was time to repurpose it for unlicensed use, like more WiFi. There is now a bill to force this proposition.

I think this would be the best thing ever for V2V, and I outlined 2 years ago how doing V2V in phones instead of cars is far more likely to get success. Phones are replaced every two years with a whole new generation, and smartphones are already in 75% of cars in the USA. V2V, even under a mandate, won’t get close to that until after 2030, and by that time it will be 15-year-old technology, long obsolete.

Google Airbags on the outside

A Google patent for placing airbags of a sort on the outside of vehicles to protect others has gotten a lot of press. While I was working at Google, I started the push for this (though the design in the patent is not mine.) I’m glad to see more investigation of it, though. The reality, as everybody knows, is that perfection is not possible, so planning for the hopefully small number of inevitable accidents and mitigating them is the right strategy.

Nvidia

For the past year, Nvidia has been pitching their latest Tegra GPU in the mobile space. They argue that a general purpose chip with very high power, thanks to their giant research budget, will produce better results than a dedicated machine vision chip from a smaller company like MobilEye that takes several years between generations. As a result, they put a big focus on automotive and self-driving at their recent GPU conference. I was in Europe and unable to attend.

More news from China

Reports suggest that Chinese internet companies Baidu and Tencent (WeChat) are starting robocar projects. (Previously Baidu provided some maps for BMW’s projects.) I’ve spoken at a couple of events for Baidu and encouraged this direction. Details are scarce though, and there is some speculation this is more in the “smart” car direction (similar to Tesla) than a full robocar. We should note that the legal situation in China is a lot different, and these large companies might be able to easily get more advanced products to be declared legal well before those in other countries can.

BMW crash

A BMW driver testing night vision collision avoidance systems crashed into a police van in Stuttgart. This was not a robocar, and of course it is amusing that if you have a crash, you might as well do it into a speeding police van! It does raise issues about risks of testing things with safety drivers — in this case, perhaps the test driver was distracted by his testing and didn’t pay enough attention to the environment. Most of the supervised class vehicles being developed today require the driver to keep paying attention, something that Google has decided not to pursue.

This article first appeared on robocars.com.