Are today's challenges of making robocars dealbreakers?
Submitted by brad on Mon, 2014-10-27 11:52There's been a lot of press recently about an article in Slate by Lee Gomes which paints a pessimistic picture of the future of robocars, and particularly Google's project. The Slate article is a follow-on to a similar article in MIT Tech Review
Gomes and others seem to feel that they and the public were led to believe that current projects were almost finished and ready to be delivered any day, and they are disappointed to learn that these vehicles are still research projects and prototypes. In a classic expression of the Gartner Hype Cycle there are now predictions that the technology is very far away.
Both predictions are probably wrong. Fully functional robocars that can drive almost everywhere are not coming this decade, but nor are they many decades away. But more to the point, less-functional robocars are probably coming this decade -- much sooner than these articles expect, and these vehicles are much more useful and commercially viable than people may expect.
There are many challenges facing developers, and those challenges will keep them busy refining products for a long time to come. Most of those challenges either already have a path to solution, or constrain a future vehicle only in modest ways that still allow it to be viable. Some of the problems are in the "unsolved" class. It is harder to predict when those solutions will come, of course, but at the same time one should remember that many of the systems in today's research vehicles were in this class just a few years ago. Tackling hard problems is just what these teams are good at doing. This doesn't guarantee success, but neither does it require you bet against it.
And very few of the problems seem to be in the "unsolvable without human-smart AI" class, at least none that bar highly useful operation.
Gomes' articles have been the major trigger of press, so I will go over those issues in detail here first. Later, I will produce an article that has even more challenges than listed, and what people hope to do about them. Still, the critiques are written almost as though they expected Google and others, rather than make announcements like "Look at the new milestone we are pleased to have accomplished" to instead say, "Let's tell you all the things we haven't done yet."
Gomes begins by comparing the car to the Apple Newton, but forgets that 9 years after the Newton fizzled we had the success of the Palm Pilot, and 10 years after that Apple came back with the world-changing iPhone. Today, the pace of change is much faster than in the 80s.
Here are the primary concerns raised:
Maps are too important, and too costly
Google's car, and others, rely on a clever technique that revolutionized the DARPA challenges. Each road is driven manually a few times, and the scans are then processed to build a super-detailed "ultramap" of all the static features of the road. This is a big win because big server computers get to process the scans in as much time as they need, and see everything from different angles. Then humans can review and correct the maps and they can be tested. That's hard to beat, and you will always drive better if you have such a map than if you don't.
Any car that could drive without a map would effectively be a car that's able to make an adequate map automatically. As things get closer to that, making maps will become cheaper and cheaper.
Naturally, if the road differs from the map, due to construction or other changes, the vehicle has to notice this. That turns out to be fairly easy. Harder is assuring it can drive safely in this situation. That's still a much easier problem than being able to drive safely everywhere without a map, and in the worst case, the problem of the changed road can be "solved" by just the ability to come to a safe stop. You don't want to do that super often, but it remains the fail-safe out. If there is a human in the car, they can guide the vehicle in this. Even if the vehicle can't figure out where to go to be safe, the human can. Even a remote human able to look at transmitted pictures can help the car with that -- not live steering, but strategic guidance.
This problem only happens to the first car to encounter the surprise construction. If that car is still able to navigate (perhaps with human help,) the map can be quickly rebuilt, and if the car had to stop, all unmanned cars can learn to avoid the zone. They are unmanned, and thus probably not in a hurry.
The cost of maps
In the interests of safety, a lot of work is put into today's maps. It's a cost that somebody like Google or Mercedes can afford if they need to, (after all, Google's already scanned every road in many countries multiple times) but it would be high for smaller players.