Collision Avoidance Motion Planning for Connected and Automated Vehicle Platoon Merging and Splitting With a Hybrid Automaton Architecture

Connected and automated vehicle (CAV) platooning exhibits significant potential in enhancing traffic efficiency and sustainability. In unsteady traffic conditions, CAV platoons frequently require splitting and merging maneuvers to avoid obstacles. This study introduces a hybrid automaton architecture for collision avoidance motion planning during CAV platoon merging and splitting. A velocity obstacle algorithm based on potential fields is developed to detect collision risks and calculate collision-free velocity solutions. Two predictive control-based optimization models are developed for collision-avoidance path planning, catering to both single-cruising vehicles and vehicle platoons. A synergetic architecture based on hybrid automaton is developed to coordinate vehicle motions during platoon splitting and merging. Numerical experiments are performed to evaluate the performance of the proposed hybrid automaton architecture under various obstacle scenarios. The results demonstrate that the proposed algorithms effectively identify collision risks within CAV platoons and determine optimal vehicle velocities. The proposed architecture demonstrates excellent performance in adjusting vehicle maneuvers and adapting CAV platoon formations to changing driving environments.