Driver-Centric Data-Driven Model Predictive Vehicular Platoon With Longitudinal-Lateral Dynamics

This paper proposes a driver-centric data-driven model predictive control (DDMPC) strategy to improve driving comfort while maintaining driving safety of vehicular platoon. This strategy combines a data-driven model predictive controller and the driver-centric driving policy. The data-driven platoon model involving longitudinal-lateral dynamics is established with subspace identification to alleviate the adverse effects of uncertain dynamics. Then, a subspace predictor-based distributed data-driven model predictive controller is developed for vehicular platoon. To overcome the cutting-corner phenomenon on curved roads, the reference point is shifted from the preceding vehicle to an optimal corridor point behind it. In this way, a driver-centric driving policy is designed with a flexible spacing and soft control constraints to balance driving safety and driving comfort in terms of different driving styles. Finally, several experiments with sixty drivers are carried out on a self-developed vehicular platoon platform. The experimental results demonstrate the effectiveness of the proposed DDMPC strategy.