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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.
But does it run linux?
Seriously though, they don't seem to go into much detail about the programming aspects of the robot. Of course they give some small details on what it ends up doing, but nothing about what language they used, etc., i.e. the interesting part.
The teams did well this year, but what disappoints me is that this year, many of the teams had relied entirely on laser range finders and GPS to navigate the course.
There was one entry, a motorcycle, which still ran completely on a vision system (cameras instead of sensors). Unfortunately, it did not do too well.
While the military can still use technology developed by the teams that completed the DARPA Grand Challenge, I think they could benefit even more from a vision system capable of doing the same thing. What use is a robot that can navigate a desert if it can't actually see anything?
... 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.
The strategy is to work on freeways first: 2010-2020. Freeways are much more controlled than cities. Cities, much harder, will come later. 2020-2030. A city should be a humming hive of sensors and intelligences, by that point. (links)
Start is actually easier than landing. If everything goes according to the procedure, it's one of the simplest maneuvres. The problem is it's most risky part, that is many things may go wrong, the plane is most failure-prone, there are lots and lots accidents waiting to happen. An autopilot would have zero problems taking off, but you need a human at the controls in case something goes wrong, and if it does, better if you don't have to waste time on switching the autopilot off. Besides, since it's easy, not much work for the pilot if everything goes smoothly, autopilot not so needed.
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One result from the second Grand Challenge that lots of people harped about was the fact that Stanley, the winning (and hence, fastest) entry, completed the course with an average speed of only about 19 mph. "19 mph?" quoted some of the the nay-sayers, "we're supposed to get excited about that?"
One thing that TFA points out, which wasn't mentioned many other places, was that the course rules stated a maximum vehicle velocity of 25 mph. Ideally, then, the fastest possible average speed for any entrant would likewise have been 25 mph. Stanley, at times, wanted and could have gone faster than that, and held back due to the rule-imposed speed limit. In that context, 19 mph is actually quite good, considering the terrain would have forced it to slow down over bumps and turns.
The article presents the history of Stanley is presented as a series of intellectual breakthroughs that I can understand, just like a lot of the pop science I read when I was a kid (and continue to read). I'm pretty sure each of these breakthroughs (such as learning from humans and assessing sensor data critically) are ideas that have been around in AI for a long time. The true story of Stanley is no doubt just as dramatic but much harder for a layperson to appreciate.
I think the first practical non-military application of autonomous cars will involve a ton of infrastructure. It won't be achieved solely by making the cars as advanced as possible, but by providing a lot of supplemental data from an array of stationary sensors (and processors) installed by a city or theme park that wants to be the first to have autonomous cars.
Eventually human drivers will be banned, and the cars will communicate and cooperate with each other (much better than human drivers!). Traffic engineers will maneuver cars manually in rare instances, and computer-controlled cars will give them a wide berth. Safety will be improved, but so will traffic efficiency. Cars will become less personal, hence smaller and more efficient; crashes will become rarer and safer, hence cars will be smaller; computers will be better drivers, so cars will run faster and closer together. We can look forward to a period of ten to thirty years in which freeways don't get any wider.
Continuing my utopian fantasy, if cars become autonomous and have less personal significance, many city dwellers will choose to use taxi services instead of owning their own cars. That means that most of the cars on the road at a given time can have a sensible capacity, rather than the maximum capacity the owner imagines that he or she might need. Per-capita energy use for personal transportation in the U.S. will drop to a fraction of the current level.
It will happen someday, but maybe not in the next hundred years, depending on how stubborn we are. It would certainly be easier and more rewarding to start with helpful, high-infrastructure environments, but the military has such a massive capacity for funding research that we will probably solve the harder problem of hostile environments first. I.e., we'll have autonomous robot sharks with frickin' laser beams on their heads long before we have Johnny Cab.
Human drivers don't work 99.9% of the time now. Drunk, on the cell phone, sleepy, or just plain not paying attention: there are a lot crashes out there.
Just wait. Eventually turning control of your vehicle to a computer system won't even really be a choice. Sure a few will pay the uber insurance and licensing premiums for a manual license, but most of us will opt for the emergency car control (ECC) license that allows you to steer the car while the autobrakes take it to a controlled stop if something like a massive system crash occurs. The activation of the autobrakes sends a signal to all other cars in the area and shuts down the system status "Ok" broadcast so the other cars know to avoid you and System knows to route a tow truck your way.
That's interesting. One of the assumptions behind future ATC systems is that the aircraft will fly under automatic control all the time so that higher traffic densities can be achieved safely. The definition of pilot qualification may have to be rethought if this happens.
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