User-Centric Multi-Objective Predictive Control for Mixed Vehicular Platoon

The penetration rate of automated vehicles (AVs) will remain unsaturated for a long period, leading to the coexistence of AVs and human-driven vehicles (HDVs), namely, mixed vehicular platoon (MVP). This paper proposes a novel user-centric multi-objective predictive control (UMPC) strategy to address dynamics uncertainty and multi-objective conflict for MVP. A data-driven model is established with subspace identification to alleviate the adverse effects of the non-ideal driving behavior and complex powertrain structure of electric vehicles. To provide a personalized driving experience, soft constraints and a user-centric multi-objective cost function are formulated. Based on this, a multi-objective optimization scheme in terms of data-driven model predictive sequence is designed. It aims to mitigate conflicts among multiple optimization objectives involving driving safety, driving comfort and energy economy. Then, a grey wolf optimizer (GWO) is devised to navigate the search process, striving for globally optimal trade-offs among conflicting objectives. With the above preparations, a UMPC strategy is suggested. Then, a hardware-in-the-loop experiment platform with CarMaker software and driving simulator is constructed, and twenty drivers participate in the experiment. The experimental results demonstrate the effectiveness of the proposed UMPC strategy.