Chen, JunZhou, ZhaodongRadovnikovich, MichoLi, JiaJones, Giselle S2026-09-282026-09-282026-01-01https://hdl.handle.net/10323/22205Autonomous vehicle (AV) motion control has been widely studied to improve path tracking accuracy, driving comfort, and safety. Model predictive control (MPC) is a promising method for AV control because it can explicitly handle vehicle dynamics, constraints, and multi-objective optimization. However, conventional time-triggered MPC requires solving an optimal control problem at every time step, which creates a high computational burden for real-time implementation. In addition, traditional MPC usually depends on manually tuned cost weights and predefined reference trajectories, making it difficult to adapt the controller to individual driving preferences. This dissertation addresses these challenges by developing learning-based personalized and computationally efficient motion control methods for AVs. First, event-triggered MPC is investigated for AV path tracking, where the optimal control problem is solved only when a triggering condition is satisfied. A switching prediction model is further used to support MPC operation across different speed ranges. To improve the controller behavior during non-triggered intervals, a linear based inter-event feedback method is developed to update the steering command using the current vehicle state without solving a new optimization problem at every sampling step. Second, inverse reinforcement learning is used to learn personalized lane change behavior from expert demonstrations. Interpretable trajectory features are designed to represent driving comfort, efficiency, lateral position, heading behavior, yaw response, and steering effort. The learned cost weights are used for personalized lane change path generation and are further incorporated into MPC to generate personalized lane change maneuvers without manual cost tuning. The proposed methods are validated using CARLA simulation, real-vehicle path-tracking experiments, scale-vehicle experiments, and truck on-road lane change testing. Results show that the proposed framework can reduce computational demand while maintaining effective tracking performance, improve inter-event feedback behavior, and generate lane change trajectories that closely match individual driver behavior.