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arXiv · 2504.13406

LangCoop: Collaborative Driving with Language

Xiangbo Gao·Yuheng Wu·Rujia Wang·Chenxi Liu·Yang Zhou·Zhengzhong Tu
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Abstract

Multi-agent collaboration holds great promise for enhancing the safety, reliability, and mobility of autonomous driving systems by enabling information sharing among multiple connected agents. However, existing multi-agent communication approaches are hindered by limitations of existing communication media, including high bandwidth demands, agent heterogeneity, and information loss. To address these challenges, we introduce LangCoop, a new paradigm for collaborative autonomous driving that leverages natural language as a compact yet expressive medium for inter-agent communication. LangCoop features two key innovations: Mixture Model Modular Chain-of-thought (M$^3$CoT) for structured zero-shot vision-language reasoning and Natural Language Information Packaging (LangPack) for efficiently packaging information into concise, language-based messages. Through extensive experiments conducted in the CARLA simulations, we demonstrate that LangCoop achieves a remarkable 96\% reduction in communication bandwidth (< 2KB per message) compared to image-based communication, while maintaining competitive driving performance in the closed-loop evaluation. Our project page and code are at https://xiangbogaobarry.github.io/LangCoop/.

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Xiangbo Gao, Yuheng Wu, Rujia Wang, Chenxi Liu, Yang Zhou, Zhengzhong Tu. 2025-04-21. LangCoop: Collaborative Driving with Language. https://arxiv.org/abs/2504.13406

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