Schörkhuber, D., Popp, R., Chistov, O., Windbacher, F., Hödlmoser, M., & Gelautz, M. (2023). Design of an automotive platform for computer vision research. Journal of Electronic Imaging, 35(16), 119-1-119–6. https://doi.org/10.2352/EI.2023.35.16.AVM-119
The goal of our work is to design an automotive platform for AD/ADAS data acquisition and to demonstrate its application to behavior analysis of vulnerable road users. We present a novel data capture platform mounted on a Mercedes GLC vehicle. The car is equipped with an array of sensors and recording hardware including multiple RGB cameras, Lidar, GPS and IMU. For subsequent research on human behavior analysis in traffic scenes, we have conducted two kinds of data recordings. Firstly, we have designed a range of artificial test cases which we recorded on a safety regulated proving ground with stunt persons to capture rare events in traffic scenes in a predictable and structured way. Secondly, we have recorded data on public streets of Vienna, Austria, showing unconstrained pedestrian behavior in an urban setting, while also considering European General Data Protection Regulation (GDPR) requirements. We describe the overall framework including data acquisition and ground truth annotation, and demonstrate its applicability for the implementation and evaluation of selected deep learning models for pedestrian behavior prediction.
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Project title:
Multimodales Sensor-Lichtsystem zum Schutz von verletzlichen Verkehrsteilnehmern: 879642 (FFG - Österr. Forschungsförderungs- gesellschaft mbH)
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Research Areas:
Visual Computing and Human-Centered Technology: 100%
