  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.
