Obstacle avoidance decision and trajectory tracking control of intelligent vehicle considering surrounding vehicles

To enhance the obstacle avoidance performance and trajectory tracking control stability of vehicles in emergency scenarios, and to solve the problem that the existing decision-making methods based on the minimum safe distance model cannot effectively make decisions in some scenarios, a novel lane-changing obstacle avoidance decision-making and control method is proposed. Initially, when emergency braking cannot avoid obstacles safely, the lane-changing trajectory is planned by quintic polynomial, and the minimum distance and collision detection (MDCD) algorithm is then employed to ascertain whether the lane-changing trajectory meets the safety conditions. Subsequently, a Linear Time-Varying Model Predictive Control (LTV MPC) trajectory tracking controller is designed based on the 7-DOF vehicle dynamics model. Finally, the effectiveness of the proposed method is verified through two representative scenarios. The simulation results indicate that the MDCD algorithm can ensure the safety and effectiveness of obstacle avoidance decisions in all emergency obstacle avoidance scenarios, and when tracking the emergency lane-changing trajectory in different typical scenarios, compared with the LTV MPC controller based on the single-track vehicle dynamics model (STVDM), the maximum tracking error can be reduced by 50.7% and 60.1%, respectively, while ensuring near-equivalent real-time performance operation.