An Efficient Rolling-Horizon Approach for Cooperative Multi-Lane Platoon Formation With Undefined Configurations

Abstract

This study proposes a cooperative platoon formation scheme in a multi-lane traffic environment with connected and autonomous vehicles (CAVs). It coordinates the lane-changing decisions and longitudinal trajectories of CAVs to form platoons based on each vehicle’s target lane, aiming to reduce the negative impact of lane-changing maneuvers on traffic flow and improve platoon formation efficiency. Mathematically, a vehicle model for platoon formation is developed which couples the lateral lane-changing and longitudinal car-following behaviors of vehicles. A model predictive control-based mixed integer linear programming (MILP) problem is formulated, which optimizes all vehicles’ lateral and longitudinal trajectories and improves maneuverability and efficiency. In contrast to the majority of existing studies, the configurations of platoons to be formed are not predefined and can be jointly optimized to further improve flexibility. Moreover, the framework that integrates configuration decisions, trajectory planning and control is executed dynamically with a rolling horizon based on real-time traffic states to enhance reliability. To validate the proposed scheme, we conduct simulation experiments in SUMO to implement the cooperative platoon formation in a typical three-lane traffic flow with different traffic demand levels. The extensive comparison results indicate the superiority of the proposed method in improving traffic flow speed and platoon formation efficiency.