Prior knowledge-assisted reinforcement learning for vehicle platoon control in cut-in scenarios

In mixed traffic environments, vehicle platoons are inevitably exposed to cut-in maneuvers performed by human-driven vehicles (HDVs), which introduce substantial uncertainty and degrade platoon control performance. To address this challenge, this paper proposes a prior knowledge-assisted reinforcement learning (PKARL) control framework for vehicle platoons. A distributed model predictive control (DMPC)-based optimization controller is first developed to provide structured guidance for deep reinforcement learning (DRL) policy learning. On this basis, the state space, action space, reward function, and training scenarios are systematically designed to enable effective responses to HDV cut-in disturbances. Furthermore, a safety filter is incorporated to enforce stability and safety constraints on the control actions generated by the DRL policy. By integrating prior control knowledge, the proposed framework improves training efficiency and enhances safety compared with conventional DRL methods. Driver-in-the-loop experimental results show that, relative to the DMPC approach, the proposed PKARL method reduces cumulative speed error by 23.0% and cumulative spacing error by 20.1%. In addition, compared with the DRL method, PKARL achieves further reductions of 10.5% in cumulative speed error and 1.1% in cumulative spacing error. These results demonstrate the effectiveness of the proposed method in mitigating HDV cut-in disturbances and improving platoon tracking performance.