STAdi-DMPC: A Trajectory Prediction Based Longitudinal Control of Connected and Automated Truck Platoon for Mixed Traffic Flow

Ensuring the high-quality operation of connected and automated trucks (CAT) and connected and automated trucks platoon (CATP) in mixed traffic flow has sparked a wave of research. With the goal of achieving efficient, safe, and stable longitudinal control of CATP in mixed traffic flow, this paper develops a data-driven longitudinal control framework (STAdi-DMPC) for CATP based on trajectory prediction. Firstly, a transformer style network based spatiotemporal attention trajectory prediction model integrating driving intention (STAdi) is constructed. Furthermore, the noise reduction reconstruction of predicted trajectories of human driving vehicle (HDV) is integrated into the DMPC based CATP longitudinal controller that meets the truck's dynamic constraints. Finally, simulation results based on the CitySim dataset are conducted and results demonstrate the superiority of the proposed longitudinal control strategy.