Inner-city intersections are among the most critical traffic areas for injury and fatal accidents. Automated vehicles struggle with the complex and hectic everyday life within those areas. Sensor-equipped smart infrastructures, which can cooperate with vehicles, can benefit automated traffic by extending the perception capabilities of drivers and vehicle perception systems. Additionally, they offer the opportunity to gather reproducible and precise data of a holistic scene understanding, including context information as a basis for training algorithms for various applications in automated traffic. Therefore, we introduce the Infrastructural Multi-Person Trajectory and Context Dataset (IMPTC). We use an intelligent public inner-city intersection in Germany with visual sensor technology. A multi-view camera and LiDAR system perceives traffic situations and road users' behavior. Additional sensors monitor contextual information like weather, lighting, and traffic light signal status. The data acquisition system focuses on Vulnerable Road Users (VRUs) and multi-agent interaction. The resulting dataset consists of eight hours of measurement data. It contains over 2,500 VRU trajectories, including pedestrians, cyclists, e-scooter riders, strollers, and wheelchair users, and over 20,000 vehicle trajectories at different day times, weather conditions, and seasons. In addition, to enable the entire stack of research capabilities, the dataset includes all data, starting from the sensor-, calibration- and detection data until trajectory and context data. The dataset is continuously expanded and is available online for non-commercial research at https://github.com/kav-institute/imptc-dataset.

翻译：城市内部交叉路口是伤害及致命交通事故中最关键的交通区域。自动驾驶车辆在该区域复杂且繁忙的日常交通环境中面临挑战。配备传感器的智能基础设施可与车辆协同，通过扩展驾驶员及车辆感知系统的感知能力，为自动化交通提供支持。此外，此类基础设施还能提供可复现且精确的整体场景理解数据，包括作为训练算法基础的情境信息，以支持自动化交通中的多种应用。为此，我们提出了基础设施多人体轨迹与情境数据集（IMPTC）。该数据集基于德国某智能公共城市交叉路口，采用视觉传感器技术，通过多视角摄像头与激光雷达系统感知交通状况及道路使用者行为，并辅以额外传感器监测天气、光照、交通信号灯状态等情境信息。数据采集系统重点关注弱势道路使用者（VRU）及多智能体交互。最终数据集包含8小时测量数据，涵盖超过2,500条VRU轨迹（包括行人、自行车骑行者、电动滑板车骑行者、婴儿车及轮椅使用者）及超过20,000条车辆轨迹，覆盖不同时段、天气条件及季节。此外，为支持完整的研究能力，数据集提供从传感器数据、标定数据、检测数据到轨迹及情境数据的全链条信息。该数据集持续扩展，并可通过https://github.com/kav-institute/imptc-dataset在线获取，仅限非商业研究用途。