LLM-Raft: Enhancing Urban Traffic Efficiency and Safety through Decentralized Coordination of Autonomous Vehicles

Lingfeng Zhou, Shuaixing Chen, Jin Gao, Dequan Wang

Keywords: Urban Mobility, Autonomous Vehicles, Multi-Agent Systems, Decentralized Coordination

TL;DR: We propose a decentralized coordination framework that allows LLM-powered autonomous vehicles to agree on high-level, human-like "traffic narratives", leading to significantly safer and more efficient traffic flow in urban simulations.

Abstract: Urban areas face persistent challenges of traffic congestion and safety, which hinder efficiency and quality of life. Coordinated autonomous vehicles (AVs) offer a promising solution, but achieving robust, decentralized coordination in dynamic urban settings remains a significant hurdle. This paper introduces LLM-Raft, a novel framework designed to enhance urban mobility by enabling LLM-powered AVs to coordinate their actions safely and efficiently. Inspired by the Raft algorithm, LLM-Raft allows vehicles to generate and agree upon ``traffic narratives''---human-like, structured propositions of their intent and justification. This semantic consensus mechanism allows for more intelligent and predictable group behaviors without a central coordinator. We validate our framework in realistic urban traffic simulations. The results show that LLM-Raft improves key urban mobility metrics, reducing collision rates by 40-50\% and task completion time by 20-30\% compared to uncoordinated baselines. Our work presents a viable path toward more collaborative and resilient multi-agent systems, contributing to the development of safer and more efficient urban transportation networks. Code is available at https://github.com/shxingch/llm-raft.

Submission Number: 6