Autonomous driving technology in urban environments is a very important avenue of research. Notably, the question of how to plan safe lane-changing trajectories is a challenge in multi-vehicle traffic environments. In our research, three kinds of polynomial lane changing mathematical models were analyzed and compared. It was found that the fifth polynomial is the most suitable for lane changing trajectories; it is defined as a generalized lane-changing trajectory cluster, whereby the minimum lane change time is determined by the vehicle lateral stability threshold. Here, a collision avoidance algorithm is proposed to eliminate unsafe trajectories. Finally, the TOPSIS algorithm is used to solve the multi-objective optimization problem, and the optimal lane-changing expected trajectory is obtained from the safe trajectory cluster. The simulation results showed improvements in lane-changing efficiency of 6.67% and no collisions in the overtaking condition. In general, the proposed method of identifying the optimal lane changing trajectory can achieve safe, efficient and stable lane changing.

The vehicle particle model was built to compare and analyze the effectiveness of three different collision avoidance methods. The results show that during vehicle high-speed emergency collision avoidance, lane change collision avoidance requires a smaller longitudinal distance than braking collision avoidance and is closer to that with a combination of lane change and braking collision avoidance. Based on the above, a double-layer control strategy is proposed to avoid collision when vehicles change lanes at high speed. The quintic polynomial is chosen as the reference path after comparing and analyzing three polynomial reference trajectories. The multiobjective optimized model predictive control is used to track the lateral displacement, and the optimization objective is to minimize the lateral position deviation, yaw rate tracking deviation, and control increment. The lower longitudinal speed tracking control strategy is to control the vehicle drive system and brake system to track the expected speed. Finally, the lane changing conditions and other speed conditions of the vehicle at 120 km/h are verified. The results show that the control strategy can track the longitudinal and lateral trajectories well and achieve effective lane change and collision avoidance.