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Proceedings Paper

Towards autonomous on-road driving via multiresolutional and hierarchical moving-object prediction
Author(s): Jerome Ajot; Craig I. Schlenoff; Raj Madhavan
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Paper Abstract

In this paper, we present the PRIDE framework (Prediction In Dynamic Environments), which is a hierarchical multi-resolutional approach for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS (Real-time Control System) and provides information to planners at the level of granularity that is appropriate for their planning horizon. The lower levels of the framework utilize estimation theoretic short-term predictions based upon an extended Kalman filter that provide predictions and associated uncertainty measures. The upper levels utilize a probabilistic prediction approach based upon situation recognition with an underlying cost model that provide predictions that incorporate environmental information and constraints. These predictions are made at lower frequencies and at a level of resolution more in line with the needs of higher-level planners. PRIDE is run in the systems’ world model independently of the planner and the control system. The results of the prediction are made available to a planner to allow it to make accurate plans in dynamic environments. 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.

Paper Details

Date Published: 29 December 2004
PDF: 12 pages
Proc. SPIE 5609, Mobile Robots XVII, (29 December 2004); doi: 10.1117/12.580172
Show Author Affiliations
Jerome Ajot, National Institute of Standards and Technology (United States)
Craig I. Schlenoff, National Institute of Standards and Technology (United States)
Raj Madhavan, National Institute of Standards and Technology (United States)


Published in SPIE Proceedings Vol. 5609:
Mobile Robots XVII
Douglas W. Gage, Editor(s)

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