This paper proposes a novel method for multi-lane 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. The code is available at: https://github.com/chuduanfeng/ConvoyLLM.

翻译：本文提出一种新颖的多车道车队编队控制方法，利用大语言模型（LLMs）解决动态高速公路环境中的协同挑战。车队中的每辆网联自动驾驶车辆采用知识驱动的方法，基于不同场景做出实时自适应决策。我们的方法使车辆能够动态执行任务，包括避障、加入/离开车队以及护航队形切换，同时保持整体车队结构。我们设计了一种基于局部动态分布式图的交错编队控制策略，确保车队保持稳定与灵活。我们在SUMO仿真平台中针对多种交通场景进行了大量实验，结果表明所提方法高效、鲁棒且能适应动态环境。代码发布于：https://github.com/chuduanfeng/ConvoyLLM。