We present an approach for robust detection, prediction, and avoidance of dynamic obstacles in urban environments. After detecting a dynamic obstacle, our approach exploits structure in the environment where possible to generate a set of likely hypotheses for the future behavior of the obstacle and efficiently incorporates these hypotheses into the planning process to produce safe actions. The techniques presented are very general and can be used with a wide range of sensors and planning algorithms. We present results from an implementation on an autonomous passenger vehicle that has traveled thousands of miles in populated urban environments and won first place in the DARPA Urban Challenge.
Detection, prediction, and avoidance of dynamic obstacles in urban environments
2008 IEEE Intelligent Vehicles Symposium ; 1149-1154
2008-06-01
1355811 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Detection, Prediction, and Avoidance of Dynamic Obstacles in Urban Environments
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