Merging order optimization and trajectory planning methods for highway CAVs in heterogeneous traffic flow

CHENG Guozhu; CHEN Yongsheng; WANG Wenzhi; XU Liang

doi:10.11860/j.issn.1673-0291.20240026

Abstract：To enhance traffic operation efficiency and improve driver and passenger comfort in highway merging areas while ensuring safety,this study proposes an optimization method for highway merging order and trajectory planning for Connected and Autonomous Vehicles(CAVs)in a heterogeneous traf-fic flow environment where Human Driven Vehicles(HDVs)and CAVs coexist.First,vehicle travel time and delay are used as performance indicators to characterize traffic operation efficiency in the merging area,and a merging order optimization function is established.The Monte Carlo Tree Search(MCTS)algorithm is used and adjusted to determine the optimal merging order.Then,based on the optimized merging order,a CAV merging trajectory planning function,referred to as Minimize Accel-eration and Jerk Trajectory Planning(MAJTP),is established.By applying optimal control theory,the analytical solution for the longitudinal optimal trajectory of CAVs is derived,forming a cooperative control strategy for highway merging.Finally,traffic simulations are conducted using the SUMO soft-ware and PYTHON libraries to validate the proposed method.Simulation results demonstrate that,at CAV penetration rates of 0.2,0.4,0.6 and 0.8,the MCTS-based merging order optimization method reduces cumulative delay by 5.75%,8.84%,12.24%,and 11.06%,respectively,compared to the First In First Out(FIFO)algorithm.Additionally,compared to the Minimize Acceleration Trajectory Planning(MATP)method,the MAJTP approach results in an average jerk value closer to zero,thereby enhancing ride comfort and verifying the effectiveness of the method.These findings provide theoretical support for traffic management and control strategies in highway merging areas.