Testing driver skill for high-speed autonomous vehicles

• Published in: Computer (Long Beach, Calif.) Vol. 39; no. 12; pp. 48 - 51
• Main Authors: Urmson, C., Whittaker, W., Harbaugh, S., Clark, M., Koon, P.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automobile driving; Autonomous; Autonomous navigation; Autonomous vehicles; Computer programs; Contests; DARPA Grand Challenge; Deserts; Error correction; High speed; Humans; Intelligent systems; Mobile robots; Navigation; Obstacles; Remotely operated vehicles; Roads; Robot sensing systems; Robots; Software testing; System testing; Test methods; Unmanned vehicles; Vehicle driving; Vehicles
• ISSN: 0018-9162; 1558-0814
• DOI: 10.1109/MC.2006.444

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