ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs

ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs Liping Lu 1, Zhican He 1, Duanfeng Chu 2, Rukang Wang 2, Saiqian Peng 2, Pan Zhou 3 Abstract -- This paper proposes a novel method for multilane convoy formation control that uses large language models (LLMs) to tackle coordination challenges in dynamic highway environments. Each connected and autonomous vehicle in the convoy uses a knowledge-driven approach to make real-time adaptive decisions based on various scenarios. Our method enables vehicles to dynamically perform tasks, including obstacle avoidance, convoy joining/leaving, and escort formation switching, all while maintaining the overall convoy structure. We design a Interlaced formation control strategy based on locally dynamic distributed graphs, ensuring the convoy remains stable and flexible. We conduct extensive experiments in the SUMO simulation platform across multiple traffic scenarios, and the results demonstrate that the proposed method is effective, robust, and adaptable to dynamic environments. I. INTRODUCTION With the rapid development of Connected and Automated V ehicles (CA Vs) technology, convoy coordination control has shown significant potential in improving traffic flow efficiency, driving safety, and fuel economy.