Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances

• URL: http://arxiv.org/abs/2502.16804v1
• Date: Mon, 24 Feb 2025 03:26:13 GMT
• Title: Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances
• Authors: Yaozu Wu, Dongyuan Li, Yankai Chen, Renhe Jiang, Henry Peng Zou, Liancheng Fang, Zhen Wang, Philip S. Yu,
• Abstract summary: Large Language Models (LLMs) have been integrated into Autonomous Driving Systems (ADSs)<n>LLMs face three major challenges: limited perception, insufficient collaboration, and high computational demands.<n>Recent advancements in LLM-based multi-agent ADSs have focused on improving inter-agent communication and cooperation.
• Score: 36.83764809130289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous Driving Systems (ADSs) are revolutionizing transportation by reducing human intervention, improving operational efficiency, and enhancing safety. Large Language Models (LLMs), known for their exceptional planning and reasoning capabilities, have been integrated into ADSs to assist with driving decision-making. However, LLM-based single-agent ADSs face three major challenges: limited perception, insufficient collaboration, and high computational demands. To address these issues, recent advancements in LLM-based multi-agent ADSs have focused on improving inter-agent communication and cooperation. This paper provides a frontier survey of LLM-based multi-agent ADSs. We begin with a background introduction to related concepts, followed by a categorization of existing LLM-based approaches 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 in this field to support future research (https://anonymous.4open.science/r/LLM-based_Multi-agent_ADS-3A5C/README.md).

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