用大模型实现动态公路车队的智能协同控制
ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs
- 基于大模型的车辆自主决策，实时应对复杂路况
- 支持变道、避障、加入/退出车队等动态操作
- 适合自动驾驶车队、智能交通系统研究者
本文提出一种新型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难题。车队中每辆联网自动驾驶汽车均采用知识驱动方式，根据多种场景实时自适应决策。该方法使车辆能动态执行障碍物避让、车队加入/离开及护送编队切换等任务，同时保持整体结构稳定。我们设计了基于局部动态分布式图的交错式编队控制策略，确保车队在动态环境中的稳定性与灵活性。我们在SUMO仿真平台中对多种交通场景进行了大量实验，结果表明该方法在动态环境中具备有效性、鲁棒性与适应性。代码已开源：https://github.com/chuduanfeng/ConvoyLLM。
原文摘要 · Abstract (English)
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.
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