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 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.
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