David Stavens

TL;DR: The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high‐speed desert driving without manual intervention and relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning.

TL;DR: In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control.

TL;DR: The architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously, is presented, which successfully finished and won second place in the DARPA Urban Challenge, a robot competition organized by the U.S. Government.

TL;DR: The architecture of Junior, a robotic vehicle capable of navigating urban environments autonomously, successfully finished and won second place in the DARPA Urban Challenge, a robot competition organized by the U.S. Government.

TL;DR: This method for identifying drivable surfaces in difficult unpaved and offroad terrain conditions as encountered in the DARPA Grand Challenge robot race achieves robustness by combining sensor information from a laser range finder, a pose estimation system and a color camera.