A fascinating article from Slate on Google’s self-driving car technology and what are really the limits of artificial intelligence.
A good technology demonstration so wows you with what the product can do that you might forget to ask about what it can’t. Case in point: Google’s self-driving car. There is a surprisingly long list of the things the car can’t do, like avoid potholes or operate in heavy rain or snow. Yet a consensus has emerged among many technologists, policymakers, and journalists that Google has essentially solved—or is on the verge of solving—all of the major issues involved with robotic driving. The Economist believes that “the technology seems likely to be ready before all the questions of regulation and liability have been sorted out.” The New York Times declared that “autonomous vehicles like the one Google is building will be able to pack roads more efficiently”—up to eight times so. Google co-founder Sergey Brin forecast in 2012 that self-driving cars would be ready in five years, and in May, said he still hoped that his original prediction would come true.
But what Google is working on may instead result in the automotive equivalent of the Apple Newton, what one Web commenter called a “timid, skittish robot car whose inferior level of intelligence becomes a daily annoyance.” To be able to handle the everyday stresses and strains of the real driving world, the Google car will require a computer with a level of intelligence that machines won’t have for many years, if ever.
The problem is not avoiding other traffic or even pedestrians (although how a computerized car deals with jay-walking pedestrians and cyclists is the sort of thing that I would be fascinated to see really work).
The problem is the artificial intelligence to deal with the road and signs itself:
…the Google car was able to do so much more than its predecessors in large part because the company had the resources to do something no other robotic car research project ever could: develop an ingenious but extremely expensive mapping system. These maps contain the exact three-dimensional location of streetlights, stop signs, crosswalks, lane markings, and every other crucial aspect of a roadway.
That might not seem like such a tough job for the company that gave us Google Earth and Google Maps. But the maps necessary for the Google car are an order of magnitude more complicated. In fact, when I first wrote about the car for MIT Technology Review, Google admitted to me that the process it currently uses to make the maps are too inefficient to work in the country as a whole.
And here’s the greatest hard problem of artificial intelligence – unlike humans who can drive roads that they have not previously encountered before or which have temporary signs or speed restrictions to which humans can read and modify their behaviour in response, computerised vehicles need to know where literally everything else is, in advance.
…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 problem with maps is that once you make them, you have to keep them up to date, a challenge Google says it hasn’t yet started working on. Considering all the traffic signals, stop signs, lane markings, and crosswalks that get added or removed every day throughout the country, keeping a gigantic database of maps current is vastly difficult. Safety is at stake here; Chris Urmson, director of the Google car team, told me that if the car came across a traffic signal not on its map, it could potentially run a red light, simply because it wouldn’t know to look for the signal. Urmson added, however, that an unmapped traffic signal would be “very unlikely,” because during the “time and construction” needed to build a traffic signal, there would be adequate opportunity to add it to the map.
Which brings me to the main point – what are the compelling economic and social reasons why driverless cars are more desirable than ones with human drivers? I can’t see any from here.
Have Google (or anyone else) considered that commuting or simply travelling from one place to another without driving yourself already has a much more economic solution?
I think Google would have better luck with another hard science fiction favourite: the flying car. At least there are no pedestrians or traffic cones up in the air – yet.
It seems to me that the problem could be solved by having a road system built with driver-less transport in mind, but that’s the sort of thing that puts the whole concept in the bracket of “completely uneconomic” or just “hard science fiction”.