This paper describes Princeton University's approach to the 2005 DARPA Grand Challenge, an off-road race for fully autonomous ground vehicles. The system, Prospect Eleven, takes a simple approach to address the problems posed by the Grand Challenge including obstacle detection, path planning, and extended operation in harsh environments. Obstacles are detected using stereo vision, and tracked in the time domain to improve accuracy in localization and reduce false positives. The navigation system processes a geometric representation of the world to identify passable regions in the terrain ahead, and the vehicle is controlled to drive through these regions. Performance of the system is evaluated both during the Grand Challenge and in subsequent desert testing. The vehicle completed 9.3 miles of the course on race day, and extensive portions of the 2004 and 2005 Grand Challenge courses in later tests. © 2007 Springer-Verlag Berlin Heidelberg.