Vehicle dynamics and external disturbance estimation for vehicle path prediction

被引:93
作者
Lin, CF
Ulsoy, AG
LeBlanc, DJ [3 ]
机构
[1] Univ Michigan, Dept Mech Engn & Appl Mech, Ann Arbor, MI 48109 USA
[2] Univ Michigan, Transportat Res Inst, Ann Arbor, MI 48109 USA
[3] Natl Pintung Polytech Inst, Vehicle Engn Dept, Pintung, Taiwan
关键词
crash avoidance; estimation; Kalman filtering; machine vision; road vehicles; safety;
D O I
10.1109/87.845881
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:508 / 518
页数:11
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