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The Effect of Process Models on Short-term Prediction of Moving Objects for Unmanned Ground Vehicles

Published

Author(s)

Rajmohan Madhavan, Craig I. Schlenoff

Abstract

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction for unmanned ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimationtheoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for shortterm prediction in both on-road and off-road driving. In this paper, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the position and orientation of moving objects for unmanned ground vehicle navigation are examined in this paper. We present results using field data obtained from different unmanned ground vehicles operating in a variety of unstructured and unknown environments.
Proceedings Title
Proceedings of the 2004 IEEE Intelligent Transportation System Conference
Conference Dates
October 3-6, 2004
Conference Location
Washington, D.C, MD
Conference Title
Washington (DC)

Keywords

Control, estimation theory, Mobility, moving object prediction, Robotics & Intelligent Systems, unmanned ground vehicles, Unmanned Systems

Citation

Madhavan, R. and Schlenoff, C. (2004), The Effect of Process Models on Short-term Prediction of Moving Objects for Unmanned Ground Vehicles, Proceedings of the 2004 IEEE Intelligent Transportation System Conference, Washington, D.C, MD, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822554 (Accessed April 17, 2024)
Created October 6, 2004, Updated February 17, 2017