Stanley (vehicle)


Stanley is an autonomous car created by Stanford University's Stanford Racing Team in cooperation with the Volkswagen Electronics Research Laboratory. It won the 2005 DARPA Grand Challenge, earning the Stanford Racing Team the 2 million dollar prize.

The Stanford Racing Team

Led by Associate Professor Sebastian Thrun, director of the Stanford Artificial Intelligence Lab, the Stanford Racing Team was developed solely for the purpose of competing in the 2005 DARPA Grand Challenge. Stanford did not participate in the 2004 DARPA Grand Challenge and was considered to have 20:1 chances of winning the 2005 competition. Stanley is currently located at the Smithsonian National Air and Space Museum, although it was displayed at the 2006 New York International Auto Show, and spent 2 years at the Volkswagen Autostadt Museum. The Stanford Racing Team entered a new vehicle, a modified Volkswagen Touareg wagon, dubbed "Junior", in the 2007 DARPA Urban Challenge. Other key contributors on the team include Michael Montemerlo, Sven Strohband, David Stavens, Hendrik Dahlkamp, and Cedric Dupont, and Pamela Mahoney.

Construction

Stanley's original frame was a standard European diesel model Volkswagen Touareg provided by Volkswagen's ERL for the competition. The Stanford Racing Team chose the Touareg for its "drive by wire" control system which could be adapted to be run directly from an on-board computer without the use of actuators or servo motors.
To navigate, Stanley used five roof mounted Sick AG LIDAR units to build a 3-D map of the environment, supplementing the position sensing GPS system. An internal guidance system utilizing gyroscopes and accelerometers monitored the orientation of the vehicle and also served to supplement GPS and other sensor data. Additional guidance data was provided by a video camera used to observe driving conditions out to eighty meters and to ensure room enough for acceleration. Stanley also had sensors installed in a wheel well to record a pattern imprinted on the tire and to act as an odometer in case of loss of signal. Using the data from this sensor, the on-board computer can extrapolate how far it has traveled since the signal was lost.
To process the sensor data and execute decisions, Stanley was equipped with six low-power 1.6 GHz Intel Pentium M based computers in the trunk, running different versions of the Linux operating system.

Programming

The School of Engineering developed the 100,000 lines of software run by Stanley to interpret sensor data and execute navigation decisions. Using what Popular Mechanics calls a "common robot hierarchy", Stanley utilizes "low-level modules fed raw data from LIDAR, the camera, GPS sets and inertial sensors into software programs the vehicle's speed, direction and decision making.
Stanley was characterized by a machine learning based approach to obstacle detection. Data from the LIDARs was fused with images from the vision system to perform more distant look-ahead. If a path of drivable terrain could not be detected for at least 40 meters in front of the vehicle, speed was decreased and the LIDARs were used to locate a safe passage.
To correct a common error made by Stanley early in development, the Stanford Racing Team created a log of "human reactions and decisions" and fed the data into a learning algorithm tied to the vehicle's controls; this action served to greatly reduce Stanley's errors. The computer log of humans driving also made Stanley more accurate in detecting shadows, a problem that had caused many of the vehicle failures in the 2004 DARPA Grand Challenge.