Safe large-scale coordination of multiple cooperative connected autonomous vehicles (CAVs) hinges on communication that is both efficient and interpretable. Existing approaches either rely on transmitting high-bandwidth raw sensor data streams or neglect perception and planning uncertainties inherent in shared data, resulting in systems that are neither scalable nor safe. To address these limitations, we propose Uncertainty-Guided Natural Language Cooperative Autonomous Planning (UNCAP), a vision-language model-based planning approach that enables CAVs to communicate via lightweight natural language messages while explicitly accounting for perception uncertainty in decision-making. UNCAP features a two-stage communication protocol: (i) an ego CAV first identifies the subset of vehicles most relevant for information exchange, and (ii) the selected CAVs then transmit messages that quantitatively express their perception uncertainty. By selectively fusing messages that maximize mutual information, this strategy allows the ego vehicle to integrate only the most relevant signals into its decision-making, improving both the scalability and reliability of cooperative planning. Experiments across diverse driving scenarios show a 63% reduction in communication bandwidth with a 31% increase in driving safety score, a 61% reduction in decision uncertainty, and a four-fold increase in collision distance margin during near-miss events. Project website: https://uncap-project.github.io/

翻译：协同联网自动驾驶车辆（CAV）大规模安全协调的关键在于通信的高效性与可解释性。现有方法要么依赖传输高带宽的原始传感器数据流，要么忽视了共享数据中固有的感知与规划不确定性，导致系统既缺乏可扩展性也不够安全。为应对这些局限，我们提出不确定性引导的自然语言协同自动驾驶规划（UNCAP），这是一种基于视觉-语言模型的规划方法，使CAV能够通过轻量级自然语言消息进行通信，同时在决策中显式地考虑感知不确定性。UNCAP采用两阶段通信协议：（i）主车CAV首先识别出最需要进行信息交换的车辆子集；（ii）被选中的CAV随后传输定量表达其感知不确定性的消息。通过选择性地融合能最大化互信息的消息，该策略使主车仅将最相关的信号整合到决策中，从而提升了协同规划的可扩展性与可靠性。在多种驾驶场景下的实验表明，该方法实现了通信带宽降低63%，驾驶安全评分提升31%，决策不确定性降低61%，并在险情避让事件中将碰撞距离裕度提高了四倍。项目网站：https://uncap-project.github.io/