Prior-knowledge-guided model-based reinforcement learning for integrated longitudinal-lateral control of vehicular platoons

To address the complexity of longitudinal-lateral vehicle dynamics in platoon control and the challenge of mitigating traffic oscillations, a novel model-based reinforcement learning (MBRL) method with planning capability is proposed for longitudinal-lateral control of vehicle platoons. Specifically, the integration of the environment model and policy model, combined with a sampling-based planning approach, enables planning-based control during decision-making, which significantly improves the stability and safety of vehicle platoon control. In the two-dimensional scenario, a unified state space and joint action space are designed to achieve longitudinal-lateral control, along with a multidimensional reward function that integrates both control objectives. To address the low exploration efficiency and the high proportion of ineffective exploration during the early stage of training, a prior-knowledge-guided exploration strategy is introduced. This strategy improves learning efficiency and accelerates convergence by incorporating guided actions and constraints. The training results indicate that the incorporation of prior knowledge significantly enhances training efficiency. In evaluations under speed-limit scenarios and real-data scenarios, the proposed method demonstrates superior performance in longitudinal-lateral control, platoon stability, safety, and adaptability, while maintaining high computational efficiency.