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Carnegie Mellon Robotics Institute

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Abstract

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.

Notes

Associated Center(s): Field Robotics Center

Associated Project(s): Urban Challenge and Tartan Racing

Number of pages: 6

Text Reference

David Ferguson, Michael Darms, Christopher Urmson, and Sascha Kolski, "Detection, Prediction, and Avoidance of Dynamic Obstacles in Urban Environments," Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, June, 2008, pp. 1149-1154.

BibTeX Reference

@inproceedings{Ferguson_2008_6188,
   author = "David Ferguson, Michael Darms, Christopher Urmson, and Sascha Kolski",
   title = "Detection, Prediction, and Avoidance of Dynamic Obstacles in Urban Environments",
   pages = "1149-1154",
   publisher = "IEEE",
   address = "Eindhofen",
   month = "June",
   year = "2008",
}

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