Optimal Vehicle Lane Change Trajectory Planning in Multi-Vehicle Traffic Environments

Featured Application This paper is mainly dedicated to research on a trajectory planning strategy for intelligent vehicle autonomous lane changing in the V2V scenario of an urban multi-vehicle traffic environment. 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.