Obstacle Detection and Tracking for the Urban Challenge

Michael Darms, Paul Rybski, Christopher R. Baker, and Christopher Urmson
IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 3, September, 2009, pp. 475-485.


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Abstract
This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University ’s winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex scenarios of urban driving to safely operate in the proximity of other vehicles. The tracking system fuses sensor data from more than a dozen sensors with additional information about the environment to generate a coherent situational model. A novel multiple-model approach is used to track the objects based on the quality of the sensor data. Finally, the architecture of the tracking subsystem explicitly abstracts each of the levels of processing. The subsystem can easily be extended

Keywords
Terms—Object tracking, obstacle classification, obstacle detection, situational reasoning, system architecture, Tartan Racing

Notes
Associated Center(s) / Consortia: Field Robotics Center
Associated Project(s): Urban Challenge and Tartan Racing
Number of pages: 11

Text Reference
Michael Darms, Paul Rybski, Christopher R. Baker, and Christopher Urmson, "Obstacle Detection and Tracking for the Urban Challenge," IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 3, September, 2009, pp. 475-485.

BibTeX Reference
@article{Darms_2009_6600,
   author = "Michael Darms and Paul Rybski and Christopher R. Baker and Christopher Urmson",
   title = "Obstacle Detection and Tracking for the Urban Challenge",
   journal = "IEEE Transactions on Intelligent Transportation Systems",
   pages = "475-485",
   month = "September",
   year = "2009",
   volume = "10",
   number = "3",
}