The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset
Manuel Hetzel, Hannes Reichert, G\"unther Reitberger, Erich Fuchs,, Konrad Doll, Bernhard Sick

TL;DR
The IMPTC dataset provides a comprehensive, multi-modal collection of trajectories and contextual data from an inner-city intersection, supporting research in automated traffic and vulnerable road user safety.
Contribution
This paper introduces the IMPTC dataset, a novel, extensive multi-sensor dataset capturing multi-agent interactions and context at an urban intersection for automated traffic research.
Findings
Over 2,500 VRU trajectories collected
More than 20,000 vehicle trajectories recorded
Dataset includes diverse weather, lighting, and seasonal conditions
Abstract
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…
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Taxonomy
TopicsAutonomous Vehicle Technology and Safety · Traffic and Road Safety · Traffic Prediction and Management Techniques
