用自然语言让自动驾驶车直接交流意图，提升交通协同效率与安全。
Automated Vehicles Should be Connected with Natural Language
- 用自然语言传递驾驶意图与决策理由，突破传统感知数据通信瓶颈。
- 相比原始传感器数据或神经特征，语言通信更高效且兼容不同系统。
- 适合研究智能交通、多车协同与可解释自动驾驶的学者与工程师。
多智能体协同驾驶通过集体感知与决策有望提升交通安全性与效率。然而，现有通信方式——包括原始传感器数据、神经网络特征和感知结果——在带宽效率、信息完整性与智能体互操作性方面存在局限。此外，传统方法普遍忽略决策层融合，忽视协同驾驶的关键维度。本文主张，解决这些问题需从以感知为中心的数据交换转向使用自然语言进行显式的意图与推理沟通。自然语言兼具语义密度与通信效率，能灵活适应实时场景，并连接异构智能体平台。通过直接传递意图、理由与决策，它将协同驾驶从被动的数据共享转变为主动协调，推动智能交通系统在安全性、效率与透明度上的进步。
原文摘要 · Abstract (English)
Multi-agent collaborative driving promises improvements in traffic safety and efficiency through collective perception and decision making. However, existing communication media -- including raw sensor data, neural network features, and perception results -- suffer limitations in bandwidth efficiency, information completeness, and agent interoperability. Moreover, traditional approaches have largely ignored decision-level fusion, neglecting critical dimensions of collaborative driving. In this paper we argue that addressing these challenges requires a transition from purely perception-oriented data exchanges to explicit intent and reasoning communication using natural language. Natural language balances semantic density and communication bandwidth, adapts flexibly to real-time conditions, and bridges heterogeneous agent platforms. By enabling the direct communication of intentions, rationales, and decisions, it transforms collaborative driving from reactive perception-data sharing into proactive coordination, advancing safety, efficiency, and transparency in intelligent transportation systems.
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