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A Hierarchical, Multi-Resolutional Moving Object Prediction Approach for Autonomous On-Road Driving

Published

Author(s)

Craig I. Schlenoff, Rajmohan Madhavan, Anthony J. Barbera

Abstract

In this paper, we present a hierarchical, multi-resolutional approach for moving object prediction via estimation-theoretic and situation-based probabilistic techniques. The results of the prediction are made available to a planner to allow it to make accurate plans in the presence of a dynamic environment. We have applied this approach to an on-road driving control hierarchy being developed as part of the DARPA Mobile Autonomous Robotic Systems (MARS) effort. Experimental results are shown in two simulation environments.
Citation
Proceedings of the 2004 ICRA Conference

Keywords

4D/RCS, dynamic environment, Kalman filter, moving object prediction, multi-resolutional, on-road driving, probabilistic prediction

Citation

Schlenoff, C. , Madhavan, R. and Barbera, A. (2004), A Hierarchical, Multi-Resolutional Moving Object Prediction Approach for Autonomous On-Road Driving, Proceedings of the 2004 ICRA Conference, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824500 (Accessed May 25, 2024)

Issues

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Created May 3, 2004, Updated February 17, 2017