This thesis presents a comprehensive approach to integrating a trajectory planner and follower for autonomous vehicles (AVs) using model predictive contouring control (MPCC). The planner generates collision-free trajectories with a kinematic bicycle model, while the follower tracks them using a dynamic bicycle model with a smaller integration step size and higher update frequency. The hierarchical architecture allows for a long planning horizon and a fast control loop. Mismatches between the planner and follower can result in tracking errors and conservative trajectories. To address this, a feedback-hierarchical interface is proposed, feeding back the mismatch error from follower to planner. Obstacles are then inflated with this error, minimizing harmful deviations and collision risks. The paper validates the Local Motion Planner using a simulator with a dynamic bicycle model, testing different follower-solver settings in urban scenarios. The results show a reduction in collision rate of 23\% compared to a single-layer MPCC planner, with similar levels of lateral error and task duration.