Appearance-Based Obstacle Detection with Monocular Color Vision
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
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From autonomous vehicles to safer cars: selected challenges for the software engineering
SAFECOMP'12 Proceedings of the 2012 international conference on Computer Safety, Reliability, and Security
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This paper presents a vision-based color segmentation algorithm suitable for urban environments that separates an image into areas of drivable and non-drivable terrain. Assuming that a part of the image is known to be drivable terrain, other parts of the image are classified by comparing the Euclidean distance of each pixel's color to the mean colors of the drivable area in real-time. Moving the search area depending on each frame's result ensures temporal consistency and coherence. Furthermore, the algorithm classifies artifacts such as white and yellow lane markings and hard shadows as areas of unknown drivability. The algorithm was thoroughly tested on the autonomous vehicle 'Caroline', which was a finalist in the 2007 DARPA Urban Challenge.