Path Tracking Cascade Control for Automated Valet Parking of AGV Systems

With the gradual advancement of technology from advanced driver assistance systems (ADAS) to full autonomous driving, integrating multiple functions across different operational design domains (ODDs) has become imperative. Integrated cruising and parking—one of the most critical integrated functions for autonomous electric vehicles—demands a consistent algorithmic framework, particularly for reference path tracking. However, previous studies have rarely explored a unified path tracking control framework capable of adapting to both scenarios. To address the path tracking requirements of integrated cruising and parking, this paper develops a cascade control framework that incorporates both vehicle kinematics and dynamics. The lateral control algorithm is detailed designed based on the kinematics-based sliding mode control with conditional integrator and dynamics-based predictive controller for cruising and parking, respectively. The performance of the proposed framework is validated through real-vehicle experiments under typical integrated cruising and parking situations. Experimental results demonstrate that the maximum lateral error is less than 0.14 m (compared to the industry threshold of 0.3 m) and the maximum heading error is less than 3.5° (versus the industry limit of 8°) in representative test scenarios, confirming its effectiveness and industrial applicability.