Papers:
D. Caveney and R. Sengupta. Architecture and Application Abstractions for Multi-Agent Collaboration Projects. Proc. of IEEE Conference on Decision and Control, Seville, Spain, Dec. 2005.
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D. Caveney, Y. Kang, and J.K. Hedrick. Probabilistic Mapping For Unmanned Rotorcraft using Point-Mass Targets and Quadtree Structures. Proc. of ASME IMECE, Orlando, FL, Nov. 2005.
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D. Caveney and J.K. Hedrick. Path Planning for Targets in Close Proximity with a Bounded Turn-Rate Aircraft. Proc. of AIAA Guidance, Navigation and Control Conference, San Francisco, CA, Aug. 2005.
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Y. Kang, D. Caveney, and J.K. Hedrick. Performance Analysis of an IMM-based Obstacle Detection Algorithm. Proc. of ASME IMECE, Anaheim, CA, Nov. 2004.
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D. Caveney, D. Dirkmanns, and J.K. Hedrick. Before the Controller: A Multiple Target Tracking Routine for Driver Assistance Systems. Automatisierungstechnik, 51:230-238, May 2003.
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D. Caveney, B. Feldman, and J.K. Hedrick. Comprehensive Framework for Multisensor Multitarget Tracking in the Adaptive Cruise Control Environment. Proc. of the 6th International Symposium on Advanced Vehicle Control (AVEC), Hiroshima, Japan. Sept. 2002, pp. 697-702.
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D. Caveney and J.K. Hedrick. Single versus Tandem Radar Sensor Target Tracking in the Adaptive Cruise Control Environment. Proc. of American Control Conference (ACC), Anchorage, AK, May 2002, pp. 292-297.
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D. Caveney and J.K. Hedrick. Multiple Target Tracking in the Adaptive Cruise Control Environment Using Multiple Models and Probabilistic Data Association. Proc. of ASME IMECE, Design Engineering-23274, New York, NY, Nov. 2001.
(pdf)
Masters Thesis (May 2001):
Multiple Target Tracking in the Adaptive Cruise Control
Environment Using Multiple Models and Probabilistic Data Association
Ph. D. Dissertation (May 2004):
Multiple Model Techniques in Automotive Estimation and Control
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Derek Caveney
The final incarnation of this project was a sensor suite of 8 radar and lidar sensors that covered 360deg around an X5 test vehicle in Munich, Germany. The tracker is capable of tracking up to 34 targets simultaneously and is presently used in a lane-change assistance application to tracking vehicles as they approached and subsequently pass the test vehicle.