Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances
It provides a comprehensive overview for researchers and practitioners in NLP and autonomous driving, but is incremental as it surveys existing methods rather than introducing new ones.
This survey paper tackles the integration of Large Language Models (LLMs) into multi-agent autonomous driving systems to address challenges like limited perception and insufficient collaboration, highlighting recent advances in language-driven communication and coordination.
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.