Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

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



Rajmohan Madhavan, Craig I. Schlenoff


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 filterbased 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 models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.
International Journal of Control Automation and Systems


autonomous ground vehicles, estimation theory, LADAR/LIDAR Sensors, moving object prediction, Robotics & Intelligent Systems, Unmanned Systems


Madhavan, R. and Schlenoff, C. (2006), The Effect of Process Models on Short-term Prediction of Moving Objects for Autonomous Driving, International Journal of Control Automation and Systems, [online], (Accessed April 19, 2024)
Created June 28, 2006, Updated February 19, 2017