Inside DARPA's Robot Race 135
Belfegor writes "The PBS series Nova has a great feature on their website, regarding the coverage of the DARPA-sponsored 'Robot Race' in which driverless vehicles 'competed' in a 130-mile race across the Mojave Desert. The full show is available on the website, and besides that they have plenty more information about the robotics behind the challenge, and also some pretty cool out-takes from the show."
Used to be a lot slower. (Score:3, Interesting)
But mostly these robots depend on the assumption that everything remains still.
Stanford 0wn3d Carnegie (Score:2, Interesting)
Also, the SuperDAD Toyota pickup looked like it had a tenth of the tech of Stanley but it was doing almost as well. If only the laser sensor hadn't detached itself from the roof.
I'm a geek, so I watched this twice last night. (Score:5, Interesting)
I will say, I was impressed, and surprised that I did not see an article on it at
I will say, that aside from "Stanley" winning the race on completion and time, I also believe that Stanley was the best technology. The H1lander and friend were micromanaged, and there were two vehicles that had different strategies (the tortoise and the hair) and it took almost the whole 2 hours of a team of people to map out the course and program the robots. They then added the fudge factor for human error with the fast and slow strategies.
Stanley was programmed in minutes of receiving the map, and it calculated its speed dynamically on its own. Stanley had "adaptive vision" which overlaid laser, video, and other sensory data to create a dynamic field of view of what was safe to drive through.
Now, what shocked me, was that so many teams finished this year. Nobody got past 7 or 9 miles last year, and many vehicles passed the entire 132 mile trip this year. Watching the vehicles drive was impressive. Most of the time, they appeared to be manned.
The course was not easy, by any stretch of the imagination. With the success of Stanley, I believe that this will increase the adaptive and learning capabilities in current software controlled systems. Currently, software is brute forced into trying to accommodate all possible logical conditions, which is impossible, and often just wrong.
Seen it-One eyed. (Score:2, Interesting)
Re:Great show but... (Score:2, Interesting)
Maybe all these guys are geniuses and get grants to work on the stuff. Maybe university supported or something like that. Or! They make their money in half a year, and build robot cars the rest of the time.
Re:You almost never see the words (Score:5, Interesting)
Interview with Director and Team Leader (Score:2, Interesting)
but would it work? (Score:3, Interesting)
It seems to me like 21st century warfare is a whole different animal - how hard would it be for a motivated, talented individual to figure out some simple attacks for the navigation systems on these vehicles, and get loads of sweet US munitions delivered to their doorstep? How effective would one of these vehicles be in an urban setting? How easy would it be to create a series of obstacles that would paralyze one of these vehicles?
It's amazing technology, for sure, and the Stanford and CMU teams deserve kudos. I'm just concerned that with the current rush to technological solutions and shift away from "boots on the ground", this technology will be in battle zones far too quickly.
Re:Great show but... (Score:2, Interesting)
Re:but would it work? (Score:2, Interesting)
This is just conjecture based on a half-recollection but I don't thik it would be too difficult to attack a relativly slow moving, unarmed autonomous vehicle such as demonstarated by the Grand Challenge vehicles which are at the state of the art.
Re:Fascinating program (Score:3, Interesting)
This sounds a little bit more like that, what I have heard. I've read, that they throw away most of the code and rewrote a large deal. E.g the classification of driveable terrain by the laser scanner was rewritten and learned. AFAIK, most of what has been published (and to what you pointed) is fairly generic stuff.
To the best of my knowledge [ira.uka.de], it has not been published how they learned the far range vision based on the near range laser scanner, which, to my eyes, is the most interesting part of the project.
> Nice try, I wasn't on CMU either.
Well, the comment on Sebastian Thruns previous affiliation and the code development sounds like something Mr. Whittaker could have said. But from what I've heard, he followed a fully stochastically approach and less reliance on the physical stability of the sensors and GPS, which AFAIK was quite different to the Red Teams approach and resulted in a much smaller code base.
Re:Seen it (Score:3, Interesting)
I was really happy Stanford won the competition. The "red" team with two entries (from Carnegie Mellon?) also finished but were behind on time... the thing is though not only was Stanford's win absolute, they also did it much "smarter".
Stanford took an approach of focusing on software, to make their vehicle more smart. They gave it the course, but left it up to the vehicle to decide how fast to go and the specifics of how soon to turn, etc.
Meanwhile Carnegie Mellon took the approach of focusing on hardware, and it took them something like 2 hours to go over the course and specifically map out a path for the vehicle to follow and the speed it should take (as opposed to I think they said 27 minutes to just give the course details to the Stanford vehicle). Carnegie Mellon had a team of at least a dozen grad students figuring it all out.
So I was much happier with Stanford's win, it seemed like more of an accomplishment.
Correct "In-Formation" (Score:1, Interesting)
http://www.mininova.org/tor/68961 [mininova.org]
Purchase this movie if you at all enjoyed it. That is your civic duty. True