Towards Autonomous On-Road Driving via Multi-Resolutional and Hierarchical Moving Object Prediction

Published

October 28, 2004

Author(s)

Jerome G. Ajot, Craig I. Schlenoff, Rajmohan (. Madhavan

Abstract

In this paper, we present the PRIDE framework (Prediction In Dynamic Environments), which is a hierarchical multiresolutional 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.

Proceedings Title

Proceedings of SPIE Optics East 2004

Conference Dates

October 25-28, 2004

Conference Location

Philadelphia, PA, USA

Conference Title

Industrial Optical Robotic Systems Technology & Applications

Pub Type

Conferences

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Keywords

autonomous vehicle, hierarchical, Knowledge Engineering, Mobility, moving object prediction, On-road driving, PRIDE, Unmanned Systems

Citation

Ajot, J. , Schlenoff, C. and Madhavan, R. (2004), Towards Autonomous On-Road Driving via Multi-Resolutional and Hierarchical Moving Object Prediction, Proceedings of SPIE Optics East 2004, Philadelphia, PA, USA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822558 (Accessed December 11, 2024)