The Effect of Process Models on Short-Term Prediction of Moving Objects for Autonomous Driving

Published

December 1, 2005

Author(s)

Rajmohan Madhavan, Craig I. Schlenoff

Abstract

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous 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 estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving.In this article, we analyze the complementary role played by vehicle kinematic modes 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 autonomous ground vehicle navigation are examined in this article. We present results using field data obtained from different autonomous ground vehicles operating in rugged outdoor environments.

Citation

International Journal of Control Automation and Systems

Volume

3

Issue

4

Pub Type

Journals

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Keywords

autonomous ground vehicles, estimation theory, moving object prediction

Citation

Madhavan, R. and Schlenoff, C. (2005), The Effect of Process Models on Short-Term Prediction of Moving Objects for Autonomous Driving, International Journal of Control Automation and Systems, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=823562 (Accessed April 18, 2024)