Vehicular communication (V2X) technologies are widely regarded as a cornerstone for cooperative and automated driving, yet their large-scale real-world deployment remains limited. As a result, understanding V2X performance under realistic, full-scale traffic conditions continues to be relevant. Most existing performance evaluations rely on synthetic traffic scenarios generated by simulators, which, while useful, may not fully capture the features of real-world traffic. In this paper, we present a large-scale, data-driven evaluation of V2X communication performance using real-world traffic datasets. Vehicle trajectories derived from the Highway Drone (HighD) and Intersection Drone (InD) datasets are converted into simulation-ready formats and coupled with a standardized V2X networking stack to enable message-level performance analysis for entire traffic populations comprising over hundred thousands vehicles across multiple locations. We evaluate key V2X performance indicators, including inter-generation gap, inter-packet gap, packet delivery ratio, and channel busy ratio, across both highway and urban intersection environments. Our results show that cooperative awareness services remain feasible at scale under realistic traffic conditions. In addition, the findings highlight how traffic density, mobility patterns, and communication range influence V2X performance and how synthetic traffic assumptions may overestimate channel congestion.

翻译：车联网（V2X）技术被广泛认为是协同自动驾驶的基石，但其大规模实际部署仍然有限。因此，理解V2X在真实、全尺度交通条件下的性能仍然具有重要意义。现有的大多数性能评估依赖于仿真器生成的合成交通场景，这些场景虽然有用，但可能无法完全捕捉真实交通的特征。本文利用真实世界交通数据集，对V2X通信性能进行了大规模、数据驱动的评估。基于高速公路无人机（HighD）和交叉路口无人机（InD）数据集提取的车辆轨迹被转换为仿真就绪格式，并与标准化的V2X网络协议栈相结合，从而能够对包含多个地点、数十万辆车辆的整个交通群体进行消息级性能分析。我们在高速公路和城市交叉路口两种环境中评估了关键的V2X性能指标，包括消息生成间隔、数据包间隔、数据包投递率和信道繁忙率。我们的结果表明，在真实交通条件下，协同感知服务在大规模场景中仍然可行。此外，研究结果揭示了交通密度、移动模式以及通信范围如何影响V2X性能，并指出合成交通假设可能高估信道拥塞程度。