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.

I. Introduction

Cooperative vehicle platooning, enabled by connected and automated technologies [1], [2], involves virtually-linked vehicles traveling together in a string with short spacing between them. CAV platooning has the potential to achieve significant energy savings and emission reductions [3]. Vehicle maneuvering complexity is greatly reduced, resulting in reduced driver workloads [4], [5]. Maintaining a stable vehicle platoon in actual traffic, especially when encountering obstacles in unstable traffic flow, is challenging [6], [7]. Vehicle platoons frequently require splitting and merging maneuvers to avoid obstacles that disrupt their intended path or motion state [8], [9].