The plausibility of driverless cars

2015-10-05

in Economics, Geek stuff

There is a widespread expectation that autonomous or driverless cars of the sort being developed by Google will soon become commercially available and active on public roads. A recent Slate article makes some strong arguments for why that expectation may be premature:

But the maps have problems, starting with the fact that the car can’t travel a single inch without one. Since maps are one of the engineering foundations of the Google car, before the company’s vision for ubiquitous self-driving cars can be realized, all 4 million miles of U.S. public roads will be need to be mapped, plus driveways, off-road trails, and everywhere else you’d ever want to take the car. So far, only a few thousand miles of road have gotten the treatment, most of them around the company’s headquarters in Mountain View, California. The company frequently says that its car has driven more than 700,000 miles safely, but those are the same few thousand mapped miles, driven over and over again.

Another issue is what will happen to driverless cars when they get into a situation where they cannot function (say, a construction site includes temporary stop lights, or you turn onto a road which isn’t mapped)? I can’t see passengers being very happy when their car simply won’t go anywhere anymore, and they need to abandon it and find some other form of transport.

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{ 9 comments… read them below or add one }

. October 5, 2015 at 11:13 am

Google’s Lame Demo Shows Us How Far Its Robo-Car Has Come

The ride was more carefully choreographed than a Taylor Swift concert. I pressed the the big black “Go” button, and the car rolled away with a whir. It made a few turns, and maxed out at around 15 mph. A Google employee stepped in front of me, and the car slowed and let him continue on his way unhindered. A car pulled up alongside me, and the Google Car slowed to ensure we didn’t collide. Then a cyclist made a similar move, and the car responded in a similar fashion. I saw the car make the exact same trip 10 times in all.

Making those predictions is likely the most crucial work the team is doing, and it’s based on the huge amount of time the cars have spent dealing with the real world. Anything one car sees is shared with every other car, and nothing is forgotten. From that data, the team builds probabilistic models for the cars to follow.

. May 6, 2016 at 6:00 pm

The trouble is, a fully driverless car needs to operate safely in all environments. “You don’t really need a map to do simple lane-keeping,” says John Ristevski, HERE’s grandiosely named vice-president of reality capture. “But if you’re on a five-lane freeway, you need to know which of those five lanes you’re in, which are safe to traverse, and at what exact point that exit ramp is coming up.”

The trouble is road markings can wear away or disappear under snow. And modern laser-surveying sensor systems (called LIDARs, after light detection and ranging) may not be accurate in those conditions. LIDARS calculate distances by illuminating a target with laser light and measuring the time it takes for the light to bounce back to the source. Radar does much the same thing with radio waves. In cars, LIDARS and radars have an effective range of around 50 metres, but that can shrink significantly in rain or when objects are obscured by vehicles ahead. Even the smartest car travelling at motorway speeds can “see” only around a second and a half ahead. What HD maps give self-driving cars is the ability to anticipate turns and junctions far beyond sensors’ horizons.

Even more important for an autonomous vehicle is the ability to locate itself precisely; an error of a couple of metres could place a car on the wrong side of the road. Commercial GPS systems are accurate only to around 5 metres, but can be wrong by 50 metres in urban canyons and fail completely in tunnels. HD maps, however, can include a so-called localisation layer that works with a variety of sensors to position a car within centimetres.

http://www.economist.com/news/science-and-technology/21696925-building-highly-detailed-maps-robotic-vehicles-autonomous-cars-reality

. July 15, 2016 at 7:25 pm

Driver Behaviours In A World of Autonomous Mobility

These are the behaviours and practices that will mainstream in our self-driving urban landscape.

. July 15, 2016 at 7:26 pm

“The prevalence of capatcha street furniture, itself autonomous and reconfigurable, introduced by residents looking to filter out autonomous vehicles from passing through their neighbourhoods. Introduced by one of the early pioneers of Baidu’s Self Driving Car project, with an acute sense of algorithm. (The opposite will also be true, with human-drivers filtered out of many contexts, it will be interesting to see how our cities are carved up.)”

. March 28, 2017 at 11:34 am

But pedestrians may be able to force self-driving cars to brake with confidence, given the regulatory contours that the cars’ firmware will have to conform to. In a paper published last fall in the Journal of Planning Education and Research, Adam Millard-Ball lays out three ways this could go: either cities will be effectively no-go zones for self-driving cars as pedestrians blithely step into the road; or pedestrians will be scared off by the cars’ cameras and the possibility of getting a facial-recognition-identified cameras from breaking the rules; or drivers will take control over their cars rather than chilling with their smartphones, believing that pedestrians will be scared off by the possibility of a human driver failing to brake in time.

. March 28, 2017 at 11:35 am

How Pedestrians Will Defeat Autonomous Vehicles

The ‘game of chicken’ which could be a serious problem for driverless cars

. March 28, 2017 at 11:48 am

Pedestrians, Autonomous Vehicles, and Cities
Adam Millard-Ball

Autonomous vehicles, popularly known as self-driving cars, have the potential to transform travel behavior. However, existing analyses have ignored strategic interactions with other road users. In this article, I use game theory to analyze the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods. At the same time, autonomous vehicle adoption may be hampered by their strategic disadvantage that slows them down in urban traffic.

. April 12, 2017 at 3:00 pm

Charlie Miller made headlines in 2015 as part of the team that showed it was possible to remote-drive a Jeep Cherokee over the internet, triggering a 1.4 million vehicle recall; now, he’s just quit a job at Uber where he was working on security for future self-driving taxis, and he’s not optimistic about the future of this important task.

To start with, self-driving cabs will be — by definition — fully computerized. Other car hacks have relied on hijacking the minority of vehicle functions that were controlled by computers, but on a self-driving car, everything is up for grabs. Also: by design, there may be no manual controls (and even if there are, they’ll be locked against random intervention by taxi passengers!).

It gets worse: passengers have unsupervised physical access to the car. In information security, we generally assume that if attackers can get unsupervised physical access to a device, all bets are off (this is sometimes called the evil maid attack, as one of the common threat-models is a hotel chambermaid who accesses a laptop while the owner is out of their room). Someone who wants to attack a self-driving taxi only needs to hail it — and worse still, ports like the OBD2 can’t be blocked, under penalty of federal law.

. April 12, 2017 at 3:01 pm

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