随着城市化进程加速及交通需求增长，混合交通流中弱势道路使用者（Vulnerable Road Users, VRUs，如行人与骑行者）的安全问题日益突出，亟需高精度、多样化的轨迹数据以支撑自动驾驶系统的研发与优化。然而，现有数据集在刻画VRU行为的多样性与动态性方面存在不足，难以满足复杂交通环境下的研究需求。为弥补这一缺口，本研究构建了OnSiteVRU数据集，覆盖交叉口、路段及城中村等多种场景，提供机动车、电动自行车与人力自行车的轨迹数据，总计约17,429条轨迹，时间分辨率达0.04秒。该数据集融合了俯视视角下的自然驾驶数据与车载实时动态检测数据，并整合交通信号灯、障碍物及实时地图等环境信息，支持交互事件的全面重建。结果表明，VRU_Data在VRU密度与场景覆盖度方面优于传统数据集，更全面地表征了VRU的行为特征，为交通流建模、轨迹预测及自动驾驶虚拟测试提供了关键支撑。该数据集已公开发布，可通过以下链接下载：https://www.kaggle.com/datasets/zcyan2/mixed-traffic-trajectory-dataset-in-from-shanghai。

With the acceleration of urbanization and the growth of transportation demands, the safety of vulnerable road users (VRUs, such as pedestrians and cyclists) in mixed traffic flows has become increasingly prominent, necessitating high-precision and diverse trajectory data to support the development and optimization of autonomous driving systems. However, existing datasets fall short in capturing the diversity and dynamics of VRU behaviors, making it difficult to meet the research demands of complex traffic environments. To address this gap, this study developed the OnSiteVRU datasets, which cover a variety of scenarios, including intersections, road segments, and urban villages. These datasets provide trajectory data for motor vehicles, electric bicycles, and human-powered bicycles, totaling approximately 17,429 trajectories with a precision of 0.04 seconds. The datasets integrate both aerial-view natural driving data and onboard real-time dynamic detection data, along with environmental information such as traffic signals, obstacles, and real-time maps, enabling a comprehensive reconstruction of interaction events. The results demonstrate that VRU\_Data outperforms traditional datasets in terms of VRU density and scene coverage, offering a more comprehensive representation of VRU behavioral characteristics. This provides critical support for traffic flow modeling, trajectory prediction, and autonomous driving virtual testing. The dataset is publicly available for download at: https://www.kaggle.com/datasets/zcyan2/mixed-traffic-trajectory-dataset-in-from-shanghai.