让自动驾驶车协作对话优化交通流，提升城市混行交通效率。
CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic
- 用大语言模型构建可交互的多智能体协同框架
- 在流量基准测试中表现优于现有方法
- 适合关注智能交通协同决策的研究者
将自动驾驶车辆融入城市交通具有显著潜力，可系统性减少拥堵、优化交通流。本文提出CoMAL（协作式多智能体大语言模型），通过自动驾驶车辆间的协作来优化混合交通流。CoMAL基于大语言模型，在交互式交通仿真环境中运行，包含感知模块（观察周围车辆）和记忆模块（存储各车辆策略）。整体流程包括：协作模块（促进车辆讨论并分配角色）、推理引擎（根据角色确定最优行为）、执行模块（结合规则模型实现车辆控制）。实验表明，CoMAL在Flow基准测试中表现优异。我们还评估了不同语言模型的影响，并与强化学习方法对比，验证了LLM智能体的强协作能力，为混合交通问题提供有效解决方案。代码已开源：https://github.com/Hyan-Yao/CoMAL。
原文摘要 · Abstract (English)
The integration of autonomous vehicles into urban traffic has great potential to improve efficiency by reducing congestion and optimizing traffic flow systematically. In this paper, we introduce CoMAL (Collaborative Multi-Agent LLMs), a framework designed to address the mixed-autonomy traffic problem by collaboration among autonomous vehicles to optimize traffic flow. CoMAL is built upon large language models, operating in an interactive traffic simulation environment. It utilizes a Perception Module to observe surrounding agents and a Memory Module to store strategies for each agent. The overall workflow includes a Collaboration Module that encourages autonomous vehicles to discuss the effective strategy and allocate roles, a reasoning engine to determine optimal behaviors based on assigned roles, and an Execution Module that controls vehicle actions using a hybrid approach combining rule-based models. Experimental results demonstrate that CoMAL achieves superior performance on the Flow benchmark. Additionally, we evaluate the impact of different language models and compare our framework with reinforcement learning approaches. It highlights the strong cooperative capability of LLM agents and presents a promising solution to the mixed-autonomy traffic challenge. The code is available at https://github.com/Hyan-Yao/CoMAL.
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