Multi-objective trajectory planning for connected and autonomous vehicles in mixed traffic flow

Abstract

As urban traffic complexity continues to rise, challenges related to traffic efficiency, fuel consumption, and safety are becoming increasingly critical. These issues underline the need for multi-objective trajectory optimization models, particularly in environments where both automated and human-driven vehicles coexist. Therefore, this paper developed a multi-objective trajectory planning model utilizing the TD3 algorithm. Here, we design the state space, action space, and reward function, where the state space encompasses variables such as speed, relative speed, distance to the stop line, relative position, phase state, and remaining phase duration, and the action space outputs optimal acceleration and deceleration. The reward function integrates multiple objectives, including safety, fuel consumption, and traffic efficiency. The model is verified using the SUMO tool, examining different levels of CAV penetration and varying traffic flows. The results demonstrate that as CAV penetration increases, vehicle trajectories become increasingly smooth, leading to reductions in average travel time, fuel consumption, and queue length. Specifically, at 100% CAV penetration with a traffic flow of 600 pcu/h, the highest optimization rate for average travel time reaches 15.38%. For average fuel consumption, the peak optimization rate of 19.53% occurs at a traffic flow of 800 pcu/h. Furthermore, under conditions of 300 pcu/h and 400 pcu/h traffic flow, 100% CAV penetration eliminates queues entirely. Beyond 400 pcu/h, minimal queues form with 100% CAV penetration. These results indicate that autonomous driving technology can effectively enhance the efficiency and sustainability of transportation systems, providing robust support for urban traffic management strategies. In particular, under high-density and mixed traffic conditions, the trajectory optimization model significantly improves traffic flow, reduces congestion, decreases energy consumption, and lowers the incidence of traffic accidents, thereby offering a theoretical foundation for the implementation of intelligent transportation systems.