Sensing for Autonomous Driving: Some Lessons from the DARPA Urban Challenge Race

Team Cornell's Skynet is one of six vehicles that successfully completed the 2007 DARPA Urban Challenge, with over 55 miles of fully autonomous driving in an urban environment. The competition included many scenarios such as staying in a lane, merging into traffic, passing other vehicles, obeying queueing order at stop signs, parking, and robot-robot interaction. Skynet was designed to drive "human-like" with smooth, predictable behaviors, even in the presence of a vast array of uncertainties. In this talk I will describe the vehicle design with a focus on the systems for perception and planning, and will present some results from the semi-finals and the final race. In particular I will discuss the pose estimation system which uses visual input to improve the estimate of the vehicle's location, and the object tracking system which can simultaneously track dozens of objects while accurately estimating their speed and heading. I will also discuss some of the limitations of such systems, which played a role in the fender bender between Skynet and MIT's Talos robot. Speaker: Dan Huttenlocher is the John P. and Rilla Neafsey Professor of Computing, Information Science and Business at Cornell University, where he holds a joint appointment in the Computer Science Department and the Johnson Graduate School of Management. His current research interests are in computer vision, social and information networks, geometric algorithms and autonomous driving. He has been recognized for his research and teaching contributions, including being named an NSF Presidential Young Investigator, New York State Professor of the Year and Fellow of the ACM. In addition to academic posts he has been chief technical officer of Intelligent Markets, a provider of advanced trading systems on Wall Street, and spent more than ten years at Xerox PARC directing work that led to the ISO JBIG2 image-compression standard.