Autonomous Vehicle (AV) technology promises safer, greener, and more efficient means of transportation for everyone. AVs are expected to have their first big impact in closed environments, such as mining areas, ports, and construction sites, where Heavy-Duty Vehicles (HDVs) operate. This thesis addresses lateral motion control for autonomous HDVs using Model Predictive Control (MPC). Lateral control for HDVs still has many open questions to be addressed, in particular, precise path tracking while ensuring a smooth, comfortable, and stable ride, coping with both external and internal disturbances, and adapting to different vehicles and conditions. To address these challenges, a comprehensive control module architecture is designed to adapt seamlessly to different vehicle types and interface with various planning and localization modules. Furthermore, it is designed to address system delays, maintain certain error bounds, and respect actuation constraints. This thesis presents the Reference Aware MPC (RA-MPC) for autonomous vehicles. This controller is iteratively improved throughout the thesis. The RA-MPC introduces a method to systematically handle references generated by motion planners which can consider different algorithms and vehicle models from the controller. The controller uses the linear time-varying MPC framework and considers control input rate and acceleration constraints to account for steering limitations. Furthermore, multiple models and control inputs are considered throughout the thesis. Ultimately, curvature acceleration is used as the control input, which together with stability ingredients, allows for stability guarantees under certain conditions via Lyapunov techniques. MPC is highly dependent on the prediction model used. This thesis proposes and compares different models. First, an offline-fitted, vehicle-specific nonlinear curvature response function is proposed and integrated into the kinematic bicycle model. The curvature response function is modeled as two Gaussian functions. To ...