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Stanford's Stanley wins DARPA Grand Challenge
tonyquan writes "DARPA has just announced that Stanford's "Stanley" autonomous ground vehicle has won the Grand Challenge, a $2 million contest for driverless vehicles over a 132 mile course in California's Mohave Desert. Stanley's winning time over the course was 6 hours, 53 minutes and 58 seconds, for an average speed of 19.1 mph. Second was Carnegie Mellon's Sandstorm (7:04:50), third went to another CMU vehicle "H1ghlander" (7:14:00) and fourth to the Gray Team's KAT-5 (7:30:16) More info from DARPA."
Less than 20mph in an SUV through the desert. These Robot control cars are worse than my Grandmother on an interstate.
Quite clearly these Robot controlled cars are part of a sophisticated plot to increase the amount of road rage in the US to enable the Robots to take over the country... and then the world.
It is not too late to stop them, we must insist that the next competition involves only Ford Broncos and takes place on the Freeways of Los Angeles during rush hour.
An Eye for an Eye will make the whole world blind - Gandhi
Looking at the final stats on the Grand Challenge website, it would seem that only five teams, out of the 23 that made the finals, were able to finish the course. The team that got the farthest before calling it quits managed about 80 miles, which means that the cut between those who made it and those who didn't was still pretty big. Another interesting thing about the final results is that, if you look at the pretty red and blue graph lines, they describe what looks like a sort of decaying function...
Or perhaps I'm just a dork.
While I'm happy that these hard-working academics were successful, I can't help but note the downside to this development.
Forget military applications. What I foresee is that, for computer scientists who've lost their jobs to outsourcing, this will deprive them of one more alternative, namely a career as a taxi/truck/bus/etc driver.
When one person suffers from a delusion, it is called insanity. When many people suffer from a delusion it is called Rel
The course did have a fair number of twists and turns in it. There were some places, like dried lake beds, where the cars could open up a bit, but for the most part it was bumpy dirt tracks one which even you or I couldn't do more than, say, 40 mph. There were also, intentionally, a fair number of obstacles designed to throw the computer systems off. You and I wouldn't have much difficulty in recognizing a cattle gate on a road, but imagine trying to teach a computer vision system to distinguish that. In other cases, the robots had to drive through tunnels that would not only be dark (making vision systems less accurate) but also lack any GPS signal.
So, yes, it did average out to a pretty slow "race." But, on the other hand, it is a marked improvement over last time, when no one even came close to finishing. I think that, in the interests of trying to ensure that they safely finished the course, let alone win, the various teams were playing it a little conservatively, and not trying to go for pedal-to-the-metal performance. Maybe next year, now that they have some confidence.
Well, so were Einstein, Werner von Braun... etc. :)
"Drivers unnecessary"
For far better info than the anemic (and completely flash based) gc.org site:
m l -- DARPA's GC message boardse nge2005/ -- Was updated throughout the actual event. Best coverage I've seen yet.
http://www.darpa.mil/grandchallenge/discussion.ht
http://www.tgdaily.com/2005/10/08/darpagrandchall
http://www.popsci.com/popsci/darpachallenge/ -- Popular Science's rather disorganized site
I'm still looking for "highlight" video myself... or pretty much any non-bland video (seeing them cross the finish line is nifty and all, but that was not a challenging part of the race). I particularly want video of Alice trying to take out some reporters!
It's not so much an improvement in the AI as it is an improvement in the sensors. These vehicles look ahead about 30 feet and plot their course based on very simple logic. If there is a negative obstacle (a hole), it is more difficult for sensors to detect than if there is a rock sticking up in the path. Last race, the only thing that stopped red team was a hairpin turn. Their sensors looked straight ahead and only a little to the sides, but when faced with the hairpin turn, the vehicle almost fell off the side of the mountain! But the rules of the AI haven't changed much- just the sensors. If you're driving through jungle, for example, you have to have sensors that don't see leaves as obstacles. Otherwise the path will look totally impassable.
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Being funny is my sig nature.
sort of.
...) while i think palo alto has much better weather than pittsburgh :)
:) I would'nt be surprised if they also use large parts of the basic control and command software infrastructure (TCX) written by thrun and others while at cmu. if it is, no wonder they required
:)
the stanford leader (thrun) and their lead software developer
(mike montelermo (sp?)) were originally from cmu.
they only recently moved to stanford. although thrun claims it's coz of his wife, some people think it was coz of too much competition and bad blood at cmu which has lots of people working in mobile robots (wittaker, simmons, nourbaksh, choset,
the particle filter based localizer and mapper was developed while at CMU. Frank Dellaert (now at georgia tech) first introduced that to mobile robotics after reading about the
condensation algorithm in computer vision (i like to believe that i had a part in that last bit
7 PCs for redundancy, that is some of the worst spaghetti code i've ever had the displeasure of working with. it's easier to make it fault-tolerant by just throwing more hardware at it.
i'm not trying to belittle stanford in any way, but i just thought people might be interested in knowing that the real story in this case is a lot more complicated. the relationship between the winning teams were a lot more incestuous
thrun BTW is an amazing all-round guy with an infectious smile all the time.
The universities competing in this competition know perfectly well they're helping the armed forces kill people.
You're making the common mistake of assuming that the purpose of the military is to kill people. It's not. The purpose of the military is primarily to defend your country, and secondarily to defend other people where this is deemed beneficial to your country's interests. Killing people is one of the ways this is done, but the primary goal in a war is to persuade the enemy to surrender, not to kill as many of them as possible. If you can use smart weapons and special forces to take out their infrastructure or their commanders, you can get the majority of the opposing forces to give up. Similarly, the average soldier, faced with an enemy that knows no fear, feels no pain, and has nothing to lose but money - in other words, an unmanned assault vehicle - is not going to go out and fight it if he can help it.
Oh, and I'll just add at this point that the most recent thing I heard in the media about the US army was this: that they just sent eight military helicopters to help survivors of the earthquake in central Asia. That's not "killing people". That's your army spending a heckuvalot of money to help people who are not only foreigners, but, by and large, actually hate America. This is called "doing good", and I speak for much of the world when I say that we admire America when it does good. And it doesn't take much imagination to think of other ways America could do good, if it had better AI and robotics technology at its command: think of small autonomous reconnaisance robots, being used to locate survivors in the rubble.
When you look at the results, and you see two colleges with virtually unlimited resources and millions of dollars spent on their vehicles, huge corporate sponsors and engineers at their beck and call from Boeing to Catepillar, who finished, and then this dinky little Team Grey from a suburb of New Orleans, with a splintered development team as a result of the Hurricane Katrina disaster, and they FINISHED just behind the big guys, leaving other heavily-funded vehicles in the dust.
Relatively speaking, a small indy group, even if their time was a tad slower than CMU or Stanford, essentially put those three teams to shame when you compare the resources they had available to them.
The real story here is who is behind the Grey team's car. It must be a far superior design than either CMU or Stanford's considering the limited resources and experience they had in addressing the challenge.