Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Driving
To address the limitations of conventional model predictive control (MPC) in autonomous driving motion planning—namely, restricted solution spaces due to convex approximations and the difficulty of balancing real-time performance with global optimality—this paper proposes a safety-enhanced reinforcement learning (RL) and MPC co-optimization framework. Methodologically, it incorporates an energy-function-based safety index constraint and designs state-dependent, online-updated Lagrange multipliers to embed safety requirements into both RL policy optimization and MPC solving, enabling joint safe optimization of reference trajectory generation and local control. Its key contribution is the first integration of a safety index function with an adaptive Lagrange multiplier mechanism, overcoming convex approximation constraints and enabling broader exploration of globally optimal solutions. Evaluated in highway scenarios, the approach achieves a 23.6% improvement in collision avoidance rate and an 18.4% reduction in jerk (trajectory smoothness), while maintaining millisecond-level real-time responsiveness—outperforming baseline MPC and standard safety-aware RL methods.