Reinforcement Learning-Based Vehicle Control in Mixed-Traffic Environments with Driving Style-Aware Trajectory Prediction

The heterogeneity of human driving styles in mixed-traffic environments manifests as divergent decision-making behaviors in complex scenarios like highway merging. By accurately recognizing these driving styles and predicting corresponding trajectories, autonomous vehicles can enhance safety, improve traffic efficiency, and concurrently achieve fuel savings in highway merging scenarios. This paper proposes a novel framework wherein a clustering algorithm first establishes statistical priors of driving styles. These priors are then integrated into a Model Predictive Control (MPC) model that leverages Bayesian inference to generate a probability-aware trajectory prediction. Finally, this predicted trajectory is embedded as a component of the state input to a reinforcement learning agent, which is trained using an Actor–Critic architecture to learn the optimal control policy. Experimental results validate the significant superiority of the proposed framework. Under the most challenging high-density traffic scenarios, our method boosts the evaluation reward by 11.26% and the average speed by 10.08% compared to the baseline Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. This advantage also persists in low-density scenarios, where a steady 10.60% improvement in evaluation reward is achieved. These findings confirm that the proposed integrated approach provides an effective decision-making solution for autonomous vehicles, capable of substantially enhancing interaction safety and traffic efficiency in emerging mixed-traffic environments.