Filter for Car Tracking Based on Acceleration and Steering Angle


Filter for Car Tracking Based on Acceleration and Steering Angle

S.J. Maybank, A.D. Worrall, G.D. Sullivan

Department of Computer Science

The University of Reading

Abstract

The motion of a car is described using a stochastic model in which the driving processes are the steering angle and the tangential acceleration. The model incorporates exactly the kinematic constraint that the wheels do not slip sideways. Two filters based on this model have been implemented, namely the standard EKF, and a new filter (the CUF) in which the expectation and the covariance of the system state are propagated accurately. Experiments show that i) the CUF is better than the EKF at predicting future positions of the car; and ii) the filter outputs can be used to control the measurement process, leading to improved ability to recover from errors in predictive tracking.

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Last Modified: 05:38pm BST, July 14, 1997