Trajectory Planning and Safety Assessment of Autonomous Vehicles Based on Motion Prediction and Model Predictive Control

Abstract

Security problem is a fundamental issue for autonomous vehicles. Trajectory planning is a significant component of autonomous vehicle system, which directly influences the automated traffic safety. In this paper, the motion prediction of other traffic participants is considered. We use Monte Carlo simulation to predict the probabilistic occupancy of the object and give a map from probability statistics to actual scenarios. The non-time-based reference trajectory can be obtained by using high-definition map and lane detection. Then model predictive control is utilized to optimize the reference trajectory according to the current state of autonomous vehicle. Different prediction horizons and coordinate transformation are adopted to optimize the planning. By doing so, the constraint conditions can be easily involved and the result is more intuitive. The probabilistic occupancy of other traffic participants are computed offline and then the obtained results are used in real-time application. Therefore, the real-time computational burden is reduced. The crash probability is put forward to verify the feasibility of real-time trajectory in safety assessment module. Two typical scenarios are analyzed: lane change on the straight road and turning at the intersection. The simulation results illustrate the efficiency of our method.