When five autonomous vehicles, including the Stanford Racing Team’s winning entry “Stanley,” finished the 2005 Grand Challenge in the still Nevada desert, they passed a milestone of artificial intelligence. The robots in the 2007 Urban Challenge, however, will have to handle traffic. It is a tougher test that calls for a new generation of technology. Enter “Junior,” the Stanford Racing Team’s new brainchild, according to a university statement.
“In the last Grand Challenge, it didn’t really matter whether an obstacle was a rock or a bush, because either way you’d just drive around it,” says Sebastian Thrun, an associate professor of computer science and electrical engineering, in the Stanford statement. “The current challenge is to move from just sensing the environment to understanding the environment.”
That’s because in the Urban Challenge, sponsored by the Defense Advanced Research Projects Agency (DARPA), the competing robots will have to accomplish missions in a simulated city environment, which includes the traffic of the other robots and traffic laws, reports Stanford. This means that on race day, Nov. 3, the robots not only will have to avoid collisions, but they will also have to master concepts that befuddle many humans, such as right of way.
“This has a component of prediction,” says Mike Montemerlo, a senior research engineer in the Stanford Artificial Intelligence Lab (SAIL). “There are other intelligent robot drivers out in the world. They are all making decisions. Predicting what they are going to do in the future is a hard problem that is important to driving. Is it my turn at the intersection? Do I have time to get across the intersection before somebody hits me?”
Junior is a 2006 Passat wagon whose steering, throttle and brakes all have been modified by engineers at the Volkswagen of America Electronics Research Laboratory in Palo Alto, Calif., to be completely computer-controllable. The engineers also have created custom mountings for a bevy of sophisticated sensors.
But what makes Junior truly autonomous will be its software, which is the focus of about a dozen students, faculty and researchers at SAIL. Modules for tasks such as perception, mapping and planning give Junior the machine-learning ability to improve its driving and to convert raw sensor data into a cohesive understanding of its situation.