Joint Longitudinal-Lateral Trajectory Planning for CAVs in Mixed Traffic at Signalized Intersections

Mandatory lane changes pose significant challenges to trajectory planning at intersections, where vehicles are required to change lanes mid-block to reach designated turn lanes before the stop bar. MLCs often generate shockwaves that induce increased vehicle delay and fuel consumption, and the presence of human-driven vehicles in mixed traffic further exacerbates this issue. To address these challenges, this study formulates the joint longitudinal-lateral trajectory planning problem in mixed traffic as a multi-agent reinforcement learning task. We propose SS-MA-PPO, a Simulation-Supervised Multi Agent Proximal Policy Optimization framework, which guides connected and automated vehicles in both acceleration and lane-change decisions. A Simulation-Guided Supervisory Module performs offline trajectory rollouts of human-driver models to assess feasibility and safety, and arbitrates online between rule-based and learned policies. The information of surrounding vehicles is incorporated in the observation to achieve vehicle cooperation, and a transfer learning mechanism is designed to accelerate training.