ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs

TL;DR

ConvoyLLM introduces a novel LLM-based approach for dynamic multi-lane convoy control, enabling autonomous vehicles to adaptively coordinate tasks like obstacle avoidance and convoy formation in real-time highway scenarios.

Contribution

This work presents a new LLM-driven method for multi-lane convoy control that enhances adaptability and robustness in dynamic highway environments.

Findings

Effective convoy stability in simulations

Robust obstacle avoidance capabilities

Adaptive convoy formation switching

Abstract

This paper proposes a novel method for multi-lane convoy formation control that uses large language models (LLMs) to tackle coordination challenges in dynamic highway environments. Each connected and autonomous vehicle in the convoy uses a knowledge-driven approach to make real-time adaptive decisions based on various scenarios. Our method enables vehicles to dynamically perform tasks, including obstacle avoidance, convoy joining/leaving, and escort formation switching, all while maintaining the overall convoy structure. We design a Interlaced formation control strategy based on locally dynamic distributed graphs, ensuring the convoy remains stable and flexible. We conduct extensive experiments in the SUMO simulation platform across multiple traffic scenarios, and the results demonstrate that the proposed method is effective, robust, and adaptable to dynamic environments. The code is available at: https://github.com/chuduanfeng/ConvoyLLM.