To enable fully automated driving of trains, numerous new technological components must be introduced into the railway system. Tasks that are nowadays carried out by the operating stuff, need to be taken over by automatic systems. Therefore, equipment for automatic train operation and observing the environment is needed. Here, an important task is the detection of collisions, including both (1) collisions with the front of the train as well as (2) collisions with the wheel, corresponding to an driving-over event. Technologies for detecting the driving-over events are barely investigated nowadays. Therefore, detailed driving-over experiments were performed to gather knowledge for fully automated rail operations, using a variety of objects made from steel, wood, stone and bones. Based on the captured test data, three methods were developed to detect driving-over events automatically. The first method is based on convolutional neural networks and the other two methods are classical threshold-based approaches. The neural network based approach provides an mean accuracy of 99.6% while the classical approaches show 85% and 88.6%, respectively.
Figures & tables
GoA
Train operation
0
On-sight with no automation
1
Non-automated according to signals
and with safeguarding in case of overlooked signals
2
Semi-automatic with acceleration and deceleration
controlled by a technical system
3
Driver-less with a train attendant
Table 1: Simplified description of GoA from DIN EN 62290-1.
Figure 1: Left side view of the leading bogie with applied sensors.
Figure 2: Front view of the leading bogie with applied sensors.
Figure 3: Test setup on the track side.
Figure 4: Convolutional network as classificator.
Figure 5: Subset of the objects used for the driving-over experiments. Fig. (a), (c), (e), (g) show the objects before the driving-over event. Fig. (b), (d), (f), (h) show the objects after the driving-over event.
Figure 6: Acceleration signals in z-direction captured from sensors mounted on the wheel-set bearings at a velocity of 5 km/h. (a) Signal caused by the driving-over event of a birch stick. (b) Signal caused by the driving-over event of small animal bones.
The ongoing digitalization of rail systems and the increasing use of artificial intelligence (AI) are fundamentally transforming the design, operation, and maintenance of rail vehicles. While fully automated operation at Grade of Automation 4 (GoA4) is well established in metro systems, its deployment in mainline rail remains limited. This is primarily due to stringent safety requirements and the complexity of open operational environments. Current perception systems based on cameras, radar, and lidar are effective in detecting objects but provide limited capability for reliably identifying impacts, collisions, and driving-over events. This paper presents a novel approach for real-time vehicle condition monitoring and impact detection that integrates structural sensor technologies with AI-based data analysis. The proposed framework addresses three key applications: (1) automated detection of impacts, structural damage, and driving-over events, (2) condition-based maintenance enabled by continuous monitoring, and (3) long-term data analytics to support vehicle design optimization. The results demonstrate the feasibility of the proposed approach and highlight its potential to enhance operational safety, enable predictive maintenance strategies, and support the transition toward fully automated operation in mainline rail systems
Maximilian Posner, Martin Dazer, Daniela Lauer +4
Institute of Machine Components University of Stuttgart Stuttgart, Germany · AutomatedTrain DB InfraGO AG Berlin, Germany · Institute of Vehicle Concepts German Aerospace Center Stuttgart, Germany +1
Automated train operation on existing railway infrastructure requires robust camera-based perception, yet the railway domain lacks public benchmark suites with standardized evaluation protocols that would enable reproducible comparison of approaches. We present RAIL-BENCH, the first perception benchmark suite for the railway domain. It comprises five challenges - rail track detection, object detection, vegetation segmentation, multi-object tracking, and monocular visual odometry - each tailored to the specific characteristics of railway environments. RAIL-BENCH provides curated training and test datasets drawn from diverse real-world scenarios, evaluation metrics, and public scoreboards (https://www.mrt.kit.edu/railbench). For the rail track detection challenge we introduce LineAP, a novel segment-based average precision metric that evaluates the geometric accuracy of polyline predictions independently of instance-level grouping, addressing key limitations of existing line detection metrics.
Annika Bätz, Pavel Klasek, Seo-Young Ham +3
Karlsruhe Institute of Technology, Engler-Bunte-Ring 21, 76131 Karlsruhe, Germany · Deutsches Zentrum für Schienenverkehrsforschung (DZSF), August-Bebel-Straße 10, 01219 Dresden, Germany · DB InfraGO AG, EUREF-Campus 17, 10829 Berlin, Germany
The monitoring of the route and track environment plays an important role in automated driving. For example, it can be used as an assistance system for route monitoring in automation level Grade of Automation (GoA) 2, where the train driver is still on board. In fully automated, driverless driving at automation level GoA4, these systems finally take over environment monitoring completely independently. With the help of artificial intelligence (AI), they react automatically to risks and dangerous events on the route. To train such AI algorithms, large amounts of training data are required, which must meet high-quality standards due to their safety relevance. In this publication we present an automatic method for assuring the quality of training data, significantly reducing the manual workload and accelerating the development of these systems. We propose an open-source tool designed to detect nine common errors found in multi-sensor datasets for railway vehicles. To evaluate the performance of the framework, all detected errors were manually validated. Six issue detection methods achieved 100% precision, while three additional methods reached precision rates 96% and 97%.