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.

翻译：自动驾驶系统通过减少人为干预、提升运行效率及增强安全性，正在引发交通领域的革命性变革。大语言模型凭借其强大的推理、指令遵循与交互能力，已被整合至自动驾驶系统中以支持高层决策。然而，基于大语言模型的单智能体自动驾驶系统面临三大挑战：感知能力有限、协作不足以及计算需求过高。为解决这些问题，近期基于大语言模型的多智能体自动驾驶系统研究利用语言驱动的通信与协调机制来增强智能体间的协作能力。本文针对自然语言处理与多智能体自动驾驶系统这一新兴交叉领域进行了前沿性综述。我们首先介绍相关背景概念，随后依据不同的智能体交互模式对现有基于大语言模型的方法进行分类阐述。接着探讨基于大语言模型的智能体与人类交互场景中的人机互动问题。最后，我们总结了关键应用、数据集与现存挑战，以期为未来研究提供支持。