Abstract
Recent advances in wireless communication, sensing and processing technologies are fostering novel research and innovation opportunities in areas such as Industry 4.0, Smart Cities and Intelligent Transportation Systems. In particular, the railway domain is envisioned to have important breakthroughs in terms of cost-efficiency, self-management, and reliability in the operation of the rolling stocks and infrastructures. Some of these key objectives are been addressed by the concept of Railway Virtual Coupling, which is a promising solution where the capacity of the tracks is highly improved by means of reducing the distance between adjacent trains, and the physical connection between train’s compositions, through accurate Vehicle-to-Vehicle communication systems. In this work a new approach towards supporting the information dynamically exchanged by the trains is proposed, with the design and implementation of a Solid-State LIDAR based sensing system to provide an accurate, robust and low-latency on-board distance detection system between trains. The combination of a long-range distance sensor, an Internet of Things (IoT) edge hardware platform and a fuzzy clustering approach for distance detection of the object of interest allows obtaining very accurate results to support the virtual coupling maneuvers. The system implementation has been tested in a real railway scenario, where several coupling and distance detection maneuvers have been performed to verify the operation of the proposed system in an actual application context. This represents one of the first dedicated distance detection tests of this kind under real dynamic conditions documented in the literature towards railway virtual coupling.
Highlights
The continuous advances in wireless communication, sensing and processing technologies are opening up new research and innovation opportunities towards cost-efficiency, sustainability, self-management and reliability in areas such as Smart Cities, Industry 4.0, and Intelligent Transportation Systems
The optimization of the rail tracks is one of the key challenges to be faced in today’s and future research projects, since nowadays the use of the infrastructures and circulation of different nearby trains is limited by the minimum safety distance handled by the control and protection systems. In standard lines these distances are managed by Train Detection Systems (TDS), where each TDS has a minimum length of 600 m
Both the distance segments detection from the LIDAR’s field of view and the data processing outcomes are depicted to show how the LIDAR is capable of detecting different objects, and the sensor node provides a filtered and processed data for the proximity information entity related to the object distance detection
Summary
The continuous advances in wireless communication, sensing and processing technologies are opening up new research and innovation opportunities towards cost-efficiency, sustainability, self-management and reliability in areas such as Smart Cities, Industry 4.0, and Intelligent Transportation Systems. Design and embedded integration of fuzzy clustering strategies to further process and tune the distance detection to the object of interest, which is the adjacent coupled trains, based on the information provided by the LIDAR sensor. While the later is in charge of taking the distance measurements within its field of view, the first one will process the LIDAR segments to obtain the distance from the sensor to the object of interest, in this case from one train to the other. To achieve this optimization goal, in this work the FCM (Fuzzy C-means clustering) algorithm is used It employs the results obtained in the previous clustering process as the initial data, according to which each measurement will have a degree of belonging equal to 1 for the cluster assigned to it, and 0 for the rest of existing clusters. One or more arrays of belonging, that is, a membership matrix
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