A Hierarchical, Multi-Resolutional Moving Object Prediction Approach for Autonomous On-Road Driving
Craig I. Schlenoff, Rajmohan Madhavan, Anthony J. Barbera
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.
, Madhavan, R.
and Barbera, A.
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 December 5, 2023)