One of the most challenging technical implementations of today is self-driving vehicles. An important segment of self-driving is the ability of the computer to “see/detect” objects of interest at a distance which enables safe vehicle operation. An algorithm for the detection of railway infrastructure objects, namely, track and signals, is proposed in this paper to enable detection of signals which are relevant for the track the train is moving along. The algorithm integrates traditional computer vision (CV) algorithms, including Canny edge detection, Hough transform, and You Only Look Once (YOLO) algorithm, based on convolutional neural networks (CNNs). Each of the concepts (CV and CNNs) deals with a different object of detection which together form a unique system that aims to detect both the rails and the relevant signals. This approach ensures that the artificial intelligence (AI) system is “aware” of which route the signal belongs to. The reliability of the proposed algorithm in detection of a relevant signal, verified by the performed tests, is up to 99.7%. The metric method used for validation was intersection over union (IoU). The obtained value of IoU applied on the entire validation dataset exceeds 0.7. Calculated values of average precision and recall were 0.89 and 0.76, respectively. The algorithm created in this way solves the problem of detection of relevant signals along the train route, especially in multitrack scenarios such as stations and yards.

Rail transport plays a crucial role in promoting sustainability and reducing the environmental impact of transport. Ongoing efforts to improve the sustainability of rail transport through technological advancements and operational improvements are further enhancing its reputation as a sustainable mode of transport. Autonomous obstacle detection in railways is a critical aspect of railway safety and operation. While the widespread deployment of autonomous obstacle detection systems is still under consideration, the ongoing advancements in technology and infrastructure are paving the way for their full implementation. The SMART2 project developed a holistic obstacle detection (OD) system consisting of three sub-systems: long-range on-board, trackside (TS), and Unmanned Aerial Vehicle (UAV)-based OD sub-systems. All three sub-systems are integrated into a holistic OD system via interfaces to a central Decision Support System (DSS) that analyzes the inputs of all three sub-systems and makes decision about locations of possible hazardous obstacles with respect to trains. A holistic approach to autonomous obstacle detection for railways increases the detection area, including areas behind a curve, a slope, tunnels, and other elements blocking the train’s view on the rail tracks, in addition to providing long-range straight rail track OD. This paper presents a demonstration of the SMART2 holistic OD performed during the operational cargo haul with in-service trains. This paper defines the demonstration setup and scenario and shows the performance of the developed holistic OD system in a real environment.

The selection of a rubber compound has a determining influence on the final characteristics of rubber-metal springs. Therefore, the correct selection of a rubber compound is a key factor for development of rubber-metal vibration isolation springs with required characteristics. The procedure for the selection of the rubber compound for vibration isolation of rubber-metal springs has been proposed, so that the rubber-metal elements have the necessary characteristics, especially in terms of deflection. The procedure is based on numerical simulation of spring deflection with Bergström-Boyce constitutive model in virtual experiment, with a goal to determine which parameters of the constitutive model will lead to spring required deflection. The procedure was verified by case study defined to select rubber compound for a rubber–metal spring used in railway engineering.

This paper describes research aimed at the experimental determination of the influence of rubber hysteresis on the friction coefficient between rubber samples for making soles and granite tiles. In the experiments, four types of shoe rubber with similar hardness and different hysteresis properties and two granite tiles with different roughnesses (smooth and anti-slip) were used. The determination of rubber hysteresis was carried out experimentally on a uniaxial testing machine. The friction coefficient was measured using a device specially developed for this type of test, which was based on the pulling force method, while the measurement conditions were based on the EN 13893:2011 standard. The friction coefficient was measured at two different speeds, 50 mm/s and 300 mm/s, with different surface conditions. Using regression analysis and the Taguchi method, the data obtained from the experiments were analyzed to determine the influence of parameters on the friction coefficient. The experimental research shows that different rubber mixtures with the same or similar hardness could have different hysteresis properties but also different friction properties.

This paper presents an innovative method for determining the distribution of the friction generated heat from the contact of a locomotive wheel and rail, as well as the heat partition factor, during wheel slipping of an accelerating locomotive. The new method combines the finite element analysis simulation and experimental determination of the temperature distribution in a downsized model of a wheel and rail. As a result of a virtual experiment by the finite element analysis, an empirical dependence between the temperature distribution and the heat partition factor was established. The determination of the dependence enabled finding of the exact value of the heat partition factor by the optimization procedure based on matching temperatures obtained by the virtual and real experiment.