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Stanley and the Conquest of the DARPA Challenge
geekboy_x writes "Wired has a great in-depth piece on the Stanford team that won the $2 million DARPA prize. If you remember last year's disaster - with most vehicles falling off the road in the first kilometer or so - this victory becomes all the more amazing. The fact that the Stanford team used a 'tailgating' strategy is the best surprise in the article."
These are the first real steps towards completely autonomous vehicles that have any sense about them. You're not going to see these things out on roads like we have today for a long time, if ever, because of how unpredictable the real world is. However, imagine if you build roads that are only used by autonomous vehicles. It could be similar to an airplane - when you reach altitude, you program your heading and let it go at it, but when you're close to your destination, off it goes and you're back in full control. That, in my opinion, is where this technology is eventually going to go.
... is that the CMU team relied heavily on extensive pre-analysis of the environment, and failed (at least in the sense that it didn't come in first). Stanford instead relied on a probability analysis of the incoming data, along with multiple technologies for different goals (lasers for short range data, video for long range data).
It seems that the DARPA grand challenge not only showed off the first realistically autonomous vehicles, but also laid to rest the idea that expert systems were the way forward. The way forward instead is self-teaching computers. Hooray for self-teaching AI overlords!
Those who can, do. Those who can't, sue.
I was never that impressed with the CMU approach. All that manual preplanning was an obvious dead end. And the giant mechanically stablized gimbal was just too clunky. It didn't help them in 2004, when they hit an obstacle placed by DARPA, and it didn't help them in 2005, when DARPA moved the racecourse from California to Nevada to prevent preplanning. The Air Force colonel in charge for 2005 said preplanning wouldn't work, and he meant it.
Computer vision of the natural world is finally about to take off, after three decades of frustration. It's probably possible to do much of the early vision processing in a current-generation GPU, which may make it affordable. Look for new apps that connect to cameras and pick out items of interest. Read that paper linked above.
Basically, after two years of work they have it going at 45MPH over rough uncharted terrain.
That is pretty darn good.
The best thing about it is, the system is capable of second guessing itself, that right there is the fundamental step that lead to success.
The flip side of all of this is, it is based on probability, and while in a desert the opportunities for accidents may be minimized, I wonder how well it will deal with unexpected random events, such as people who don't put on their turn signal when changing lanes.
CPU power and other hardware can always be scaled up to deal with increase speeds (indeed a major topic that the article deals with), the question is can the algorithms deal with truly unexpected input?
Of course one solution to this is to have all cars automated, then you do not have problems with fools not using their turn signal, as the cars would just wirelessly inform each other.
Bleck, then again, I have not yet seen a perfectly working wireless network stack, hopefully who ever they get to program the cars would be of a higher caliber than the idiots who program PCs and wireless routers/switches.
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As a participant of another DARPA team (Cornell -- our site is down), I am skeptical as to whether the winners of the challenge would be able to drive in a real world environment. In many ways the Grand Challenge was a toy problem, but this is not usually emphasized because they want to make it seem more dramatic.
First of all, no other moving objects on the course. When a vehicle was about to pass another, the one in front was paused so that the passing vehicle could overtake it. At no time did the vehicles have to deal with changing conditions.
Secondly, to my knowledge, there were no obstacles (which were promised) on the course. If someone knows differently, I'd like to hear about it. So we don't know to what extent obstacle avoidance is effective on those vehicles.
Thirdly, daylight and clear weather is one thing, but nighttime, rain, snow, etc. would significantly degrade the data.
Essentially the problem that the current vehicles solved was this:
Given a set of waypoints and a "corridor" outside which you will never have to go (so far the problem can be solved only by 10cm-accuracy DGPS), use your other sensors to avoid obstacles by moving left or right within the corridor.
Not very much like real world driving at all. And I'm not saying Stanford, CMU and the others didn't accomplish something big -- I'm just saying it's not what the Wired piece makes it out to be.