用自然语言降低自动驾驶通信带宽，提升多车协作效率。
LangCoop: Collaborative Driving with Language
- 用自然语言替代图像传输，实现高效信息传递。
- 通信带宽降低96%（每消息<2KB），性能仍保持竞争力。
- 适合关注车联网、低带宽协同驾驶的开发者与研究者。
多智能体协作有望通过多辆联网车辆间的信息共享，显著提升自动驾驶系统的安全性、可靠性和通行能力。然而，现有通信方式受限于高带宽需求、智能体异构性及信息丢失等问题。为此，我们提出LangCoop，一种基于自然语言的新型协作自动驾驶范式。其核心创新包括：用于零样本视觉-语言推理的混合模型模块化思维链（M$^3$CoT），以及将信息高效打包为简洁语言消息的自然语言信息封装（LangPack）。在CARLA仿真环境中进行的大量实验表明，相较于基于图像的通信，LangCoop实现了96%的通信带宽降低（每消息<2KB），同时在闭环评估中保持了具有竞争力的驾驶表现。
原文摘要 · Abstract (English)
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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