Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments.

Michael Darms, Paul Rybski, and Christopher Urmson
Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, June, 2008, pp. 1197-1202.


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Abstract
Future driver assistance systems are likely to use a multisensor approach with heterogeneous sensors for tracking dynamic objects around the vehicle. The quality and type of data available for a data fusion algorithm depends heavily on the sensors detecting an object. This article presents a general framework which allows the use sensor specific advantages while abstracting the specific details of a sensor. Different tracking models are used depending on the current set of sensors detecting the object. A sensor independent algorithm for classifying objects regarding their current and past movement state is presented. The described architecture and algorithms have been successfully implemented in Tartan Racing? autonomous vehicle for the Urban Grand Challenge. Results are presented and discussed.

Notes
Associated Center(s) / Consortia: Field Robotics Center
Associated Project(s): Urban Challenge and Tartan Racing
Number of pages: 6

Text Reference
Michael Darms, Paul Rybski, and Christopher Urmson, "Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments.," Proceedings of the 2008 IEEE Intelligent Vehicles Symposium, June, 2008, pp. 1197-1202.

BibTeX Reference
@inproceedings{Darms_2008_6182,
   author = "Michael Darms and Paul Rybski and Christopher Urmson",
   title = "Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments.",
   booktitle = "Proceedings of the 2008 IEEE Intelligent Vehicles Symposium",
   pages = "1197-1202",
   publisher = "IEEE",
   month = "June",
   year = "2008",
}