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



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


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


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


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], (Accessed April 16, 2024)
Created October 27, 2004, Updated October 12, 2021