Physics
Scientific paper
Dec 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004spie.5609...97a&link_type=abstract
Mobile Robots XVII. Edited by Gage, Douglas W. Proceedings of the SPIE, Volume 5609, pp. 97-108 (2004).
Physics
Scientific paper
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.
Ajot Jerome
Madhavan Raj
Schlenoff Craig I.
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