轨迹预测是自动驾驶与协同驾驶系统的关键使能技术。然而，现有主流基准数据集大多以传感器为中心、地理范围受限，或基于合成移动轨迹，无法真实反映现实世界中车路协同（V2X）通信的动力学特性。本文提出CAMASA——一个大规模基于基础设施的数据集，源自摩德纳智能汽车区域（Modena Automotive Smart Area, MASA）Living Lab采集的协同感知消息（Cooperative Awareness Messages, CAMs）与去中心化环境通知消息（Decentralized Environmental Notification Messages, DENMs）。该数据集包含在真实城市交通条件下持续数月采集的逾4000万条CAM及200万条DENM。我们设计了一套严格的预处理流程，涵盖数据过滤、伪匿名化关联（以应对ETSI隐私规范导致的stationID动态变更）以及时间归一化（生成10 Hz采样率的轨迹），适用于运动预测与时序分析任务。CAMASA重建了逾14,000公里车辆行驶路径，并涵盖数万个唯一station ID，为协同式智能交通系统（Cooperative Intelligent Transportation Systems, C-ITS）研究提供了具有统计显著性的实证基础。除轨迹预测外，该数据集还可用于校准微观城市交通仿真器（如SUMO），并支持构建面向真实部署场景的智能交通系统（Intelligent Transportation Systems, ITS）数字孪生体，实现交通移动模式与V2X通信覆盖的联合建模。

Trajectory prediction is a key enabler of autonomous and cooperative driving systems. However, most existing benchmarks are either sensor-centric, geographically constrained, or based on synthetic mobility traces that do not capture real-world V2X communication dynamics. This paper introduces CAMASA, a large-scale infrastructure-based dataset derived from Cooperative Awareness Messages (CAMs) and Decentralized Environmental Notification Messages (DENMs) collected within the Modena Automotive Smart Area (MASA). The dataset comprises more than 40 million CAMs and 2 million DENMs recorded under authentic urban traffic conditions over multiple months. We present a rigorous preprocessing pipeline that includes filtering, pseudonym reconciliation to account for ETSI privacy-driven stationID changes, and temporal normalization to 10 Hz trajectories, suitable for motion forecasting and time-series analysis. With over 14,000 km of reconstructed vehicle paths and tens of thousands of unique station IDs, CAMASA provides a statistically significant empirical foundation for research on Cooperative Intelligent Transportation Systems (C-ITS). Beyond trajectory prediction, the dataset enables calibration of microscopic urban traffic simulators (e.g., SUMO) and supports the development of realistic Intelligent Transportation Systems (ITS) Digital Twins by jointly modeling mobility patterns and V2X communication coverage in real deployments.