Chen, JunAlawsi, Hussein AliCheok, KaCRadovnikovich, MichoSchmidt, Darrell2026-06-122026-06-122025-01-01https://hdl.handle.net/10323/22100Autonomous parking remains a challenging task due to the need for accurate trajectory tracking, smooth steering, and stable heading control under diverse maneuvering conditions. Conventional model predictive control (MPC) can handle system constraints effectively, but its performance depends heavily on manually tuned cost weights. This dissertation proposes a reinforcement learning-assisted model predictive control (RL-assisted MPC) framework to improve autonomous vehicle parking performance. A Deep Q-Network (DQN) agent is trained to dynamically select the cost function weights of an MPCcontroller, enabling real-time adaptation based on the vehicle’s current state. The hybrid approach leverages the predictive optimization capability of MPC together with the adaptive decision-making of RL, enabling the controller to adjust trade-offs in real time without manual re-tuning. The framework is evaluated across five different parking scenarios and compared against static-weight MPC baselines. Experimental evaluations demonstrate that the proposed RL-assisted MPC framework achieves comparable or better lateral tracking accuracy, while consistently providing smoother steering behavior and improved heading stability compared to baseline controllers using static MPC weights. The results demonstrate that RL-assisted MPC improves robustness and generalization in automated parking systems, highlighting the potential of combining model-based predictive control with RL for autonomous driving.Automated parking systemAutonomous drivingModel Predictive ControlOptimal ControlReninforcemnet learningRL-assisted MPC