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Local Motion Planning Framework for Autonomous Vehicle Considering Position Uncertainty in Highway

A motion planning framework for autonomous vehicles in highway considering position uncertainty of surrounding vehicles is proposed in this paper. Firstly, the unscented Kalman filter (UKF), which ensures generating a safe trajectory in real time, is applied to predict the trajectory of surrounding vehicles and determine its position uncertainty in a confidence ellipse. Besides, a load transfer ratio (LTR) index is introduced to indicate whether the vehicle in highway has reached its critical rollover state. Furthermore, the position uncertainty and artificial potential field are filled into the model predictive controller objective to achieve obstacle avoidance while guaranteeing safety and stability. Finally, the simulation results implemented in the Prescan-Simulink-Carsim cosimulation platform show the effectiveness of the presented framework.