For driverless train operation on mainline railways, several tasks need to be
implemented by technical systems. One of the most challenging tasks is to
monitor the train's driveway and its surroundings for potential obstacles due
to long braking distances. Machine learning algorithms can be used to analyze
data from vision sensors such as infrared (IR) and visual (RGB) cameras,
lidars, and radars to detect objects. Such algorithms require large amounts of
annotated data from objects in the rail environment that may pose potential
obstacles, as well as rail-specific objects such as tracks or catenary poles,
as training data. However, only very few datasets are publicly available and
these available datasets typically involve only a limited number of sensors.
Datasets and trained models from other domains, such as automotive, are useful
but insufficient for object detection in the railway context. Therefore, this
publication presents OSDaR23, a multi-sensor dataset of 21 sequences captured
in Hamburg, Germany, in September 2021. The sensor setup consisted of multiple
calibrated and synchronized IR/RGB cameras, lidars, a radar, and position and
acceleration sensors front-mounted on a railway vehicle. In addition to raw
data, the dataset contains 204091 polyline, polygonal, rectangle and cuboid
annotations for 20 different object classes. This dataset can also be used for
tasks going beyond collision prediction, which are listed in this paper.

本文介绍了一个在铁路上进行无人驾驶列车运营所需要的技术系统中的关键任务 —— 通过机器学习算法，分析来自红外线、视觉摄像头、激光雷达和雷达等视觉传感器的数据来检测可能的障碍物。文中详细介绍了一种包含多个传感器的数据集 OSDaR23，并强调了其在铁路领域中进行物体检测和其他任务的重要性。