Issue No. 12 - December (2006 vol. 39)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MC.2006.444
S. Harbaugh , Carnegie Mellon Univ., Pittsburgh, PA
W. Whittaker , Carnegie Mellon Univ., Pittsburgh, PA
C. Urmson , Carnegie Mellon Univ., Pittsburgh, PA
The 2005 DARPA Grand Challenge, a 212-kilometer race through the Mojave Desert, showcased the state of the art in high-speed, autonomous navigation of trails and roads. To win the challenge, a team's robot had to complete the course faster than any other robot, and it had to do so within 10 hours. Carnegie Mellon University's Red Team developed two robots, which used a combination of autonomous and human preplanning to become two of only four robots to complete the Grand Challenge. The robots used onboard sensors to adjust a preplanned route to avoid obstacles and correct for position-estimation errors. To be successful, teams had to develop innovative algorithms and systems - and rigorously test them to verify performance. The Red Team used the tests regressively to evaluate how unit changes in hardware and software affected the robots' overall driving ability
road vehicles, collision avoidance, driver information systems, mobile robots, position-estimation error, driver skill testing, high-speed autonomous vehicle, onboard sensor, mobile robot, obstacle avoidance, Remotely operated vehicles, Vehicle driving, Mobile robots, Robot sensing systems, Navigation, Roads, Humans, Error correction, System testing, Software testing, DARPA Grand Challenge, Unmanned vehicles, Autonomous vehicles
M. Clark, S. Harbaugh, W. Whittaker, P. Koon and C. Urmson, "Testing driver skill for high-speed autonomous vehicles," in Computer, vol. 39, no. , pp. 48-51, 2006.