Robohub.org
 

Meeting on a narrow road


by
25 August 2015



share this:
image: kopretinka/flickr

image: kopretinka/flickr

What should a robocar do when it meets another robocar on a very narrow road? If they’re in control, humans may resolve this in a number of ways, but it’s a widely held belief that this is one of the many problems baffling the developers of autonomous vehicles. Are they right? 

Jean-Louis Gassée, a respected computer entrepreneur, wrote a critical post on robocars recently which matches a very common pattern:

  • The author has been hearing about robocars for a while, and is interested
  • While out driving, or sometimes just while thinking, they encounter a situation which seems challenging
  • They can’t figure out what a robocar would do in that situation
  • They conclude that thus the technology is very far in the future.
those attempting to catch the developers out should first check to see what prior thinking may exist.

His scenario is the very narrow road – so narrow that it really should be one-way but it isn’t. On most of the road, two cars can pass one another and humans achieve this through various dynamics, discussion and experience.

In most of these examples, the situation is not one that is new to robocar developers. They’ve been thinking about all the problems they might in encounter in driving for over a decade, in many cases. It’s extremely rare for a newcomer to come up with a scenario they have not thought of. In addition, developers are already putting cars on the road, with over a million miles on the clock, in Google’s case, in order to find those very situations that didn’t come up when they were just thinking and driving themselves. It is not impossible for novices to come up with something new — in fact a fresh eye can often be very valuable — but those attempting to catch the developers out should first check to see what prior thinking may exist.

Some of the problems are indeed hard, and developers have temporarily shelved them to be dealt with later on the roadmap and they will not release their cars to operate on roads where unsolved situations may occur. If snow is a problem, for example the first cars will be released in places it does not snow, or they will not drive on their own if it’s snowing. In the meantime, the problems will be solved in priority order, based on how often they happen and how important they are.

The “two cars meet” situation applies very rarely on roads in the USA, so it’s not a high priority problem, neither is it a surprise issue. That’s because current plans have cars driving only with a map of the road they can drive on without such an issue. If it’s not on the map then they don’t drive on the road.

This means they know the road well, and exactly how wide it is at every spot, and what its rules are (one-way vs. two-way and so on.) They will accurately know their own width and that of oncoming vehicles. If they can’t drive safely on a road, they won’t do it. If it’s a unusual road, the cost of that will be accepted. Driving on every road, everywhere is a nice dream, but not necessary in order to have a highly useful product. While Google’s ideal prototype is planned for release for urban situations without a wheel, cars that need to go to places where they can’t drive will continue to sport wheels or other interfaces (joysticks or tablet apps for instance) that let a human guide them to get through any problems.

The two-cars meeting dilemma is interesting because, actually, it’s one where the cars can far outperform humans.

The two-cars meeting dilemma is interesting because, actually, it’s one where the cars can far outperform humans. It’s also one of the rare times that communication between cars turns out to be useful. (Typically car to server, server to car, not direct v2v, but that’s another matter.)

The reason is that super narrow roads, including country roads and urban back-allies,  have occasional wide-spots and turn outs where people can pass. They have to, to be two-way. And these will all be on the map. Cars on such a road will benefit from traffic data about other cars on the road; it will enable them to predict when they might encounter another car coming the other way. Most interestingly, one or both of the cars can adjust their speed so that they will encounter one another precisely at one of the wider spots where passing can take place.

In fact, if they do this well, they can drive a one-lane road at a nice fast speed, barely slowing down in these wider passing zones, in part because they’ll know the width of the vehicles they will be able to confidently pass quite closely. If a robocar is meeting a human driven car, it would leave some slack, picking the right passing zone, arriving early in case the other car is faster than expected and waiting if it is slower.

This remarkable ability would allow us to build low-traffic roads and alleys which are mostly only one lane wide, but which could carry traffic fairly quickly and safely in both directions. Gassée’s problem is far from a problem — it’s actually a great opportunity to vastly decrease the cost and land requirements of road construction. I wrote about this a couple of years ago, in fact.

Generally, any human car should defer to the robocar’s superior knowledge and ability to manage a close pass-by.

Even without communication, a robocar would do pretty well here. Its map would tell it, should it encounter another vehicle on the road it can’t pass, just where the closest passing spot is. It could back up if need be or, if the other car should back up, it could nudge in that direction or even display instructions to a human driver on a screen. It would be able to do this far better than a human driver because of its possession of accurate measurements and driving ability. Generally, any human car should defer to the robocar’s superior knowledge and ability to manage a close pass-by.

Not all these problems that people put forward were as easily resolved as this one, so I am not calling for people to “shut up and let the experts get to work.” There are many problems yet to be solved. Most of them can be be solved by punting, because you don’t need to drive everywhere. Although Google has shown that having a steering wheel that can be grabbed while moving is a bad idea, I do expect most cars to have some form of control that can be activated when a car is stationary. If a road needs the human touch, it will be available.

This post originally appeared on robocars.com.




Brad Templeton, Robocars.com is an EFF board member, Singularity U faculty, a self-driving car consultant, and entrepreneur.
Brad Templeton, Robocars.com is an EFF board member, Singularity U faculty, a self-driving car consultant, and entrepreneur.





Related posts :



Robot Talk Episode 124 – Robots in the performing arts, with Amy LaViers

  06 Jun 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Amy LaViers from the Robotics, Automation, and Dance Lab about the creative relationship between humans and machines.

Robot Talk Episode 123 – Standardising robot programming, with Nick Thompson

  30 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Nick Thompson from BOW about software that makes robots easier to program.

Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners

  29 May 2025
Find out who won the awards presented at the International Conference on Autonomous Agents and Multiagent Systems last week.

Congratulations to the #ICRA2025 best paper award winners

  27 May 2025
The winners and finalists in the different categories have been announced.

#ICRA2025 social media round-up

  23 May 2025
Find out what the participants got up to at the International Conference on Robotics & Automation.

Robot Talk Episode 122 – Bio-inspired flying robots, with Jane Pauline Ramos Ramirez

  23 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Jane Pauline Ramos Ramirez from Delft University of Technology about drones that can move on land and in the air.

Robot Talk Episode 121 – Adaptable robots for the home, with Lerrel Pinto

  16 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Lerrel Pinto from New York University about using machine learning to train robots to adapt to new environments.

What’s coming up at #ICRA2025?

  16 May 2025
Find out what's in store at the IEEE International Conference on Robotics & Automation, which will take place from 19-23 May.



 

Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence