A Multi-Sensor Dataset for Monitoring the Operational Environment of Rail Vehicles

Abstract:Reliable environment monitoring is essential for the safe and efficient operation of automated railway systems, covering all Grades of Automation (GoA), from partially automated (GoA2) to fully automated operation (GoA4). Artificial Intelligence (AI) plays a central role in enabling these systems to detect, classify, and react to potential hazards in real time. The development of such AI-based perception systems requires large volumes of accurately annotated data for training and validation. Within the Digitale Schiene Deutschland (DSD) program, DB InfraGO AG and understandAI GmbH have developed a comprehensive multi- sensor dataset tailored to the needs of railway environment perception. This dataset contains over 7 million high-quality annotations of both railway-specific and general perception objects, captured under varying operational scenarios. The finalized dataset can now be requested at the DB InfraGO AG and serve as a valuable resource for advancing AI-driven environment monitoring in the railway domain.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2608.04704 [cs.CV]

(or arXiv:2608.04704v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.04704 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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