Urban Challenge

Area A

DARPA‘s Urban Grand Challenge ran over the weekend, with CMU/GM taking first place, and Stanford/VW taking second. This situation was exactly reversed for the 2005 Grand Challenge, which means the rivalry between Stanford and Carnegie Mellon is building to epic proportions. More info here and here.

I’ve been following this challenge for a few years now, using the Nova special on the 2005 race for my classes. The big change for this race involves the car responding to other agents in its environment, including other moving cars (driven by professional stunt drivers), and for obeying all traffic laws, including right-of-way laws at intersections.

I went to a couple of the site visits and the first thing (one of) the vehicles did for me was a three-way turn. Now, imagine you’re watching this vehicle all by itself do a three-way turn and then come to an intersection, and there was a car there already and when it pulled up, another car pulled up after it. It knew enough to wait for the first car to go because by the rules, it knew that car had precedent. But it also knew that it had precedence over the other car that showed up after. It was stunning.

I mean it was spooky because they went down the road, they made a turn. And he turned to me and he said, ‘Now look, there’s nobody inside there right?’ I said, ‘No, no, there’s nobody inside there.’ He said, ‘Now, and there’s nobody controlling them remotely right’ because it looked like they were being driven by somebody. Now these were the two vehicles that got the furthest, by the way. |link|

There remains the critical problem that the robots are still treating other drivers merely as moving objects, and not as full-bodied agents. This allows the robots to make a host of assumptions about these other drivers, for instance that they will also obey precedent laws perfectly, but these assumptions will not reliably hold in the real world situations that the military desires. The link above suggests that the military is still on track to make 1/3rd of its ground vehicles autonomous by 2015, but there is still more work to do.

1 Comment

  1. For those following the race, I just stumbled across an interesting sidenote: nearly all the challengers in this year’s race, including all the top finishers, used Velodyne’s laser sensing equipment. Velodyne’s sensors are basically an array of 64 spinning lasers that can construct a 3D image of the surrounding environment. You can see an example of the output from these sensors in Stanford’s videos, especially in course B (wmv).

    This is interesting for a couple of reasons. It eliminates a lot of the advantages that the Stanford team had in the previous race with its use of so-called “adaptive vision” that integrated video and laser information on the fly, which gave it the edge over CMU’s gimbaled lasers. But perhaps more importantly, Velodyne was started by David and Bruce Hall, the brothers that comprised Team DAD in the previous two races, finishing 3rd and 11th, respectively. This year they decided not to enter the race, opting instead of focus on building and selling their sensor array they developed for Team DAD. Since they sold to all the major DARPA players, it looks like this was a wise (and lucrative) decision.

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