标题： 自动驾驶车辆应与自然语言连接 显示英文标题

标题： Automated Vehicles Should be Connected with Natural Language

摘要： 多智能体协作驾驶通过集体感知和决策有望提高交通的安全性和效率。 然而，现有的通信媒介——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、信息完整性和智能体互操作性方面存在局限。 此外，传统方法在很大程度上忽略了决策级融合，忽视了协作驾驶的关键维度。 在本文中，我们认为解决这些挑战需要从纯粹以感知为导向的数据交换转向使用自然语言的显式意图和推理通信。 自然语言在语义密度和通信带宽之间取得平衡，能灵活适应实时条件，并连接异构智能体平台。 通过实现意图、理由和决策的直接通信，它将协作驾驶从被动的感知数据共享转变为积极的协调，推动智能交通系统在安全、效率和透明度方面的进步。 显示英文摘要

摘要： 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.