Vehicle dynamics and external disturbance estimation for vehicle path prediction

This paper addresses the onboard prediction of a motor vehicle's path to he lp enable a variety of emerging functions in autonomous vehicle control and active safety systems. It is shown in simulation that good accuracy of pat h prediction is achieved using numerical integration of a linearized two de gree of freedom vehicle handling model. To improve performance, a steady-st ate Kalman filter is developed to estimate the vehicle's lateral velocity a nd the magnitudes of external disturbances acting on the vehicle, specifica lly the lateral force and the yaw moment disturbances. A comparison is made between three models of external disturbance time variation; a piecewise-c onstant-in-time model is found to be sufficient. Finally, an algorithm is p roposed to characterize path prediction uncertainty using a statistical cha racterization of the measurement and modeling errors. Simulation suggests t hat these algorithms may provide a useful suite of path prediction tools fo r a variety of applications.