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.
@article{arxiv.2502.16804,
title = {Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances},
author = {Yaozu Wu and Dongyuan Li and Yankai Chen and Renhe Jiang and Henry Peng Zou and Wei-Chieh Huang and Yangning Li and Liancheng Fang and Zhen Wang and Philip S. Yu},
journal= {arXiv preprint arXiv:2502.16804},
year = {2025}
}