mlsanityData to 14 Oct 2026

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

arXiv 2608.04704 ↗First posted 5 Aug 2026cs.CV cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 15 references that are in the map. First posted on arXiv 5 Aug 2026.

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References · 3

  1. 2023OSDaR23: Open Sensor Data for Rail 2023cited by 17
  2. 2020FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trainscited by 10
  3. 2023Rail Detection: An Efficient Row-based Network and A New Benchmarkcited by 8

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