An Adaptive Lane-Changing Strategy for Multi-CAV with Complex Platoon Geometric

Lane-changing simultaneously with complex platoon geometric is a complex and critical task for multi-connected autonomous vehicle (CAV). To tackle this problem, an adaptive lane-changing strategy (ALCS) is proposed in this paper. The adaptive performance of the ALCS is realized via three parts: 1) A dynamic motion state inference method (DMSIM) is introduced to generate the platoon geometric based on dynamic safe distance, taking into account the various position and velocity of multi-CAV within the platoon. 2) A polynomial-based path planner (PBPP) is designed to provide a velocity-adjustable path for lane-changing. This path planner enhances the adaptive of various velocity for multi-CAV. 3) A model predictive-based distributed controller (MPBDC) is designed to avoid collision between multi-CAV. This controller improves the controller’s capacity to handle velocity changes. Finally, the superiority of the strategy is demonstrated by simulation experiment.