Hybrid model predictive control for flexible platoon lane change with mixed logic dynamics model

In dense traffic, connected and autonomous vehicle (CAV) platoons often face challenges in lane change due to insufficient safety spacing and inefficient maneuvering strategies. This paper develops a flexible lane change strategy that dynamically determines the optimal number of CAVs for lane change while ensuring safety during the maneuver. Specifically, a discrete decision-making model is developed with logical rules for varying lane change spacing. A mixed logical dynamics (MLD) model, integrated with decision-making model, vehicle kinematics, and safety constraints, is proposed to describe the flexible lane change maneuvers. Further, a distributed hybrid model predictive control (MPC) method is designed based on the MLD model to optimize the number of lane change vehicles and regulate safe platoon control. Experimental results demonstrate that the proposed approach reduces the total lane change duration by 26% and the steering angle variation by 17%, compared to the rear-to-front sequential lane change (RFS-LC) strategy.