🤖 AI Summary
This work addresses the critical lack of high-quality, large-scale, multi-sensor annotated data tailored to varying levels of railway automation (GoA2–GoA4), which has hindered the safe and efficient deployment of AI-driven systems in rail environments. To bridge this gap, the authors construct and publicly release a multimodal dataset specifically designed for railway operational scenarios, integrating synchronized data from visible-light cameras, LiDAR, and other sensors across diverse operating conditions. Through a rigorous, high-precision manual annotation pipeline, the dataset comprises over 7 million labeled instances, enabling robust detection and classification of both rail-specific and general objects. This resource fills a significant void in training and validation data for fully automated rail systems, substantially advancing the development, evaluation, and real-world deployment of AI models in railway applications.
📝 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.