LangCoop: Collaborative Driving with Language
Journal:
arXiv
Published Date:
Apr 18, 2025
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/.
