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.
An Adaptive Lane-Changing Strategy for Multi-CAV with Complex Platoon Geometric
2023-10-27
4776163 byte
Conference paper
Electronic Resource
English
Deep Q‐network based multi‐layer safety lane changing strategy for vehicle platoon
DOAJ | 2024
|Deep Q‐network based multi‐layer safety lane changing strategy for vehicle platoon
Wiley | 2024
|Cooperative vehicle platoon control considering longitudinal and lane-changing dynamics*
Taylor & Francis Verlag | 2024
|Taylor & Francis Verlag | 2024
|Active lane-changing model based on cellular automata to promote CAV platoon
Springer Verlag | 2025
|