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.

翻译：轨迹预测是实现自主驾驶与协同驾驶系统的关键支撑技术。然而，现有基准数据集大多以传感器为中心、受地理范围限制，或基于无法反映真实车联网（V2X）通信动态特征的合成移动轨迹。本文提出CAMASA，一个基于基础设施的大规模数据集，源自摩德纳汽车智慧区（MASA）内采集的协作感知消息（CAM）和分散环境通知消息（DENM）。该数据集包含数月间在真实城市交通条件下记录的超过4000万条CAM和200万条DENM。我们构建了严格的预处理流程，包括数据过滤、应对ETSI隐私驱动站ID变化的假名统一处理，以及适用于运动预测和时间序列分析的10赫兹轨迹时间归一化。凭借超过14,000公里的重构车辆路径和数万个独立站ID，CAMASA为协作式智能交通系统（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），并通过联合建模实际部署中的移动模式与V2X通信覆盖范围，支持开发现实智能交通系统（ITS）数字孪生。