To achieve a driverless train operation on mainline railways, actual and potential obstacles for the train's driveway must be detected automatically by appropriate sensor systems. Machine learning algorithms have proven to be powerful tools for this task during the last years. However, these algorithms require large amounts of high-quality annotated data containing railway-specific objects as training data. Unfortunately, all of the publicly available datasets that tackle this requirement are restricted in some way. Therefore, this paper presents OSDaR23, a multi-sensor dataset of 45 subsequences acquired in Hamburg, Germany, in September 2021, that was created to foster driverless train operation on mainline railways. The sensor setup consists of multiple calibrated and synchronized infrared (IR) and visual (RGB) cameras, lidars, a radar, and position and acceleration sensors mounted on the front of a rail vehicle. In addition to the raw data, the dataset contains 204091 polyline, polygonal, rectangle, and cuboid annotations in total for 20 different object classes. It is the first publicly available multi-sensor dataset annotated with a variety of object classes that are relevant for the railway context. OSDaR23, available at data.fid-move.de/dataset/osdar23, can also be used for tasks beyond collision prediction, which are listed in this paper.
@article{arxiv.2305.03001,
title = {OSDaR23: Open Sensor Data for Rail 2023},
author = {Rustam Tagiew and Martin Köppel and Karsten Schwalbe and Patrick Denzler and Philipp Neumaier and Tobias Klockau and Martin Boekhoff and Pavel Klasek and Roman Tilly},
journal= {arXiv preprint arXiv:2305.03001},
year = {2024}
}