期刊
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
卷 8, 期 3, 页码 508-518出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/87.845881
关键词
crash avoidance; estimation; Kalman filtering; machine vision; road vehicles; safety
This paper addresses the onboard prediction of a motor vehicle's path to help enable a variety of emerging functions in autonomous vehicle control and active safety systems. It is shown in simulation that good accuracy of path prediction is achieved using numerical integration of a linearized two degree of freedom vehicle handling model. To improve performance, a steady-state Kalman filter is developed to estimate the vehicle's lateral velocity and the magnitudes of external disturbances acting on the vehicle, specifically the lateral force and the yaw moment disturbances. A comparison is made between three models of external disturbance time variation; a piecewise-constant-in-time model is found to be sufficient. Finally, an algorithm is proposed to characterize path prediction uncertainty using a statistical characterization of the measurement and modeling errors. Simulation suggests that these algorithms may provide a useful suite of path prediction tools for a variety of applications.
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