This November, Mike Montemerlo's Volkswagen Passat wagon will drive a 60-mile trek through an urban landscape located somewhere in the western U.S. But Montemerlo won't be sitting behind the wheel, nor will anyone else. That's because Montemerlo's Passat happens to be a special robot model, custom-developed by the Stanford University Racing Team (Fig. 1).
Along with robotic-vehicle researchers at other institutions worldwide, the Stanford team will compete in the DARPA (Defense Advanced Research Projects Agency) Urban Challenge, an event sponsored by the U.S. Department of Defense. DARPA's mission is to maintain the military's technological edge and to prevent new technology from jeopardizing national security. To meet these goals, the agency sponsors revolutionary, high-payoff research ventures such as the Urban Challenge that can lead to innovative new military capabilities as well as leading-edge consumer or business products and services.
The Urban Challenge's roots lay in a 2001 Congressional mandate that requires one-third of the nation's combat ground vehicles to be unmanned by 2015. To help reach this goal, Congress authorized the Department of Defense and DARPA to award funding and cash prizes to Urban Challenge participants. Eleven teams received seed money of up to $1 million for development while another 78 are funded either by themselves or by corporate sponsors. The competition's top three finishers can look forward to receiving cash prizes of $2 million, $1 million, and $500,000, respectively.
Self Control The Urban Challenge is designed to test the safety of autonomous vehicles as they interact with other vehicles and the local environment. Come this November, dozens of vehicles will be placed onto a course that simulates real-world city streets.
"All of the participants' vehicles will be on the course at the same time, with some additional traffic vehicles that we provide," says Norman Whitaker, the DARPA's Urban Challenge program manager. "It really tests lots of different dimensions of the technology... both their ability to sense and react correctly in traffic as well as their ability to be able to adapt to changing [road] conditions."
To succeed in the Urban Challenge, competing teams' entries must perform like vehicles controlled by human drivers and safely conduct simulated battlefield supply missions on the 60-mile course. The vehicles must obey strict rules while merging into traffic, navigating traffic circles, negotiating busy intersections, and avoiding obstacles before finishing under six hours. The competition's location will be announced in August.
Building A Robot Car The strongest Urban Challenge competitors are teamed with major automakers and other sponsors. Volkswagen, in Stanford's case, provides research expertise and funding. "Volkswagen does all the retrofitting of the vehicle and all the vehicle electronics," says Montemerlo, the team's software lead and a senior research engineer in the Stanford Artificial Intelligence Lab. "At Stanford, we concentrate on what we do best, which is the software and sensing."
Stanford's robo-Passat, nicknamed "Junior," is like a NASCAR racer in that the vehicle is so highly modified it bears little more than a passing exterior resemblance to its street counterpart. Junior's steering, throttle, and brakes were all modified by engineers at the Volkswagen of America Electronics Research Laboratory in Palo Alto, Calif., to be completely computer-controllable (Fig. 2). The engineers also created custom mountings for a bevy of sophisticated sensors.
Junior's standard equipment includes a range-finding laser array that spins to provide a 360° 3D view of the surrounding environment, nearly in real-time. Six video cameras that accompany the array "see" all around the car. Junior also uses bumper-mounted lasers, radar, GPS receivers, and inertial navigation hardware to collect data about where it is and what's nearby.
According to Montemerlo, developing and installing the control and monitoring hardware was the easy part. Teaching Junior how to safely drive itself is proving to be far more tricky, requiring programmers not only to instruct the robot on how to control itself, but also how to anticipate changes in traffic conditions. "You have to make models of other vehicles and predict how you think they're going to behave," Montemerlo says.
Junior's custom-coded software modules include a planner (for making decisions and choosing routes), a mapper (which transforms sensor readings into environment understanding), a localizer (which refines the GPS position by visual observations), and a controller (which actuates planner decisions). The entire system runs on rack-mounted servers equipped with Intel Core 2 Duo processors. Data is processed from the vehicle's instruments as frequently as 200 times per second, Montemerlo notes.
But even with all of its cutting-edge hardware and software support, Junior remains at a cognitive disadvantage compared to even a novice human driver. "The robot actually has a lot less information about what other vehicles might be doing," Montemerlo says. "A robot certainly won't be able to interpret other cars' turn signals, for instance." Nonetheless, he's hopeful that Junior will perform well this November. "We do our best to train for the worst possibility and hope that it works out," he says.
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