用大模型让自动驾驶车群协作，提升决策与沟通能力
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances
- 通过语言驱动实现多车协同决策
- 解决单车感知弱、算力高、难协作问题
- 适合关注智能交通与人车交互的研究者
自动驾驶系统正通过减少人工干预、提升效率与安全性重塑交通。大语言模型（LLMs）因其强大的推理、指令遵循与通信能力，被引入自动驾驶系统以支持高层决策。然而，基于LLM的单智能体系统面临三大挑战：感知能力有限、协作不足以及计算开销过高。为此，近期进展聚焦于基于大模型的多智能体自动驾驶系统，利用语言驱动的通信与协调机制增强智能体间的协作。本文对这一新兴领域——自然语言处理与多智能体自动驾驶的交叉前沿进行综述。首先介绍相关概念背景，随后按不同智能体交互模式对现有方法进行分类；接着探讨大模型智能体与人类在实际场景中的交互方式；最后总结关键应用、数据集及核心挑战，为未来研究提供支持。
原文摘要 · Abstract (English)
Autonomous Driving Systems (ADSs) are revolutionizing transportation by reducing human intervention, improving operational efficiency, and enhancing safety. Large Language Models (LLMs) have been integrated into ADSs to support high-level decision-making through their powerful reasoning, instruction-following, and communication abilities. However, LLM-based single-agent ADSs face three major challenges: limited perception, insufficient collaboration, and high computational demands. To address these issues, recent advances in LLM-based multi-agent ADSs leverage language-driven communication and coordination to enhance inter-agent collaboration. This paper provides a frontier survey of this emerging intersection between NLP and multi-agent ADSs. We begin with a background introduction to related concepts, followed by a categorization of existing LLM-based methods based on different agent interaction modes. We then discuss agent-human interactions in scenarios where LLM-based agents engage with humans. Finally, we summarize key applications, datasets, and challenges to support future research.
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