Decision Making and Control of Autonomous Vehicles Under the Condition of Front Vehicle Sideslip

Abstract

The behaviors of front vehicles are important factors that can influence the driving safety of autonomous vehicles on highways. This situation poses a serious threat to the security of autonomous vehicles, especially when front vehicle sideslip occurs. To address this problem, a decision-making approach can be used to promote the emergency obstacle avoidance capability of autonomous vehicles. First, the front sideslip vehicle trajectory was predicted by the kinematic models Constant Acceleration (CA), Constant Turn Rate and Velocity (CTRV), and Constant Turn Rate and Acceleration (CTRA) based on the front vehicle sideslip identification results. The CTRA prediction approach is chosen by comparing the prediction errors of the three models. To enhance the obstacle avoidance ability of autonomous vehicles, a novel trajectory planning method based on a driving characteristic vector is proposed. Model prediction control (MPC) is used to track the planned trajectory. Finally, the cosimulation platform of Simulink and Carsim was built. The simulation results show that autonomous vehicles can avoid collisions with front sideslip vehicles through the proposed approach, and the proposed trajectory planning approach has better obstacle avoidance ability than does the traditional approach.