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 multiagent 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
