Can LLMs improve multi-agent autonomous driving?
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances
This paper surveys recent advances in using Large Language Models (LLMs) for multi-agent autonomous driving systems (ADS). It explores how multiple LLM-powered agents can interact and collaborate to improve driving performance compared to single-agent LLM approaches. Key points relevant to LLM-based multi-agent systems include: addressing limitations of single-agent systems (limited perception, insufficient collaboration, high computational demands) through inter-agent communication and cooperation; different interaction modes (cooperative, competitive, debate) and structures (centralized, decentralized, hierarchical, shared memory); agent-human interaction paradigms (instructor, partnership); applications in collaborative perception, decision-making, and assistance tools; and key challenges like hallucination, multi-modality integration, and scalability.