On the Experimental Validation of Adaptive MPC-based Path Tracking Controller in CCAM Environment

This paper investigates the practical implementation and real-time feasibility of an adaptive MPC-based path-tracking control system for Connected Autonomous Vehicle (CAV) in a smart Cooperative Connected Automated Mobility (CCAM) environment. The proposed path-tracking control architecture is designed according to the best practice of the Model-Based Design (MBD), where a PID control governs the CAV longitudinal behaviour while the adaptive MPC drives its lateral dynamics. According to the V-Cycle workflow development steps, the proposed control architecture is validated through Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) platforms. This latter involves the control hardware Nvidia Drive AGX Orin and Carla simulator. After disclosing the proper functioning of the control system in real-time over the target hardware, a real-world experimental campaign is performed on a real SUV-prototype vehicle moving in a City Area test site located in the Campania region, Italy. The proving ground is such that all the necessary equipment for the emulation of CCAM environment is available. Experimental results prove the real-time feasibility and efficiency of the proposed solution.