Toward the Enhancement of Rail Sustainability: Demonstration of a Holistic Approach to Obstacle Detection in Operational Railway Environments

Miloš Simonović, Milan Banić (), Dušan Stamenković, Marten Franke, Kai Michels, Ingo Schoolmann, Danijela Ristić-Durrant, Cristian Ulianov, Sergiu Dan-Stan, Alin Plesa and Marjan Dimec
Additional contact information
Miloš Simonović: Faculty of Mechanical Engineering, University of Niš, 18000 Niš, Serbia
Milan Banić: Faculty of Mechanical Engineering, University of Niš, 18000 Niš, Serbia
Dušan Stamenković: Faculty of Mechanical Engineering, University of Niš, 18000 Niš, Serbia
Marten Franke: Institute of Automation Technology, University of Bremen, 28359 Bremen, Germany
Kai Michels: Institute of Automation Technology, University of Bremen, 28359 Bremen, Germany
Ingo Schoolmann: OHB Digital Services GmbH, 28359 Bremen, Germany
Danijela Ristić-Durrant: OHB Digital Services GmbH, 28359 Bremen, Germany
Cristian Ulianov: Future Mobility Group, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Sergiu Dan-Stan: Department of Mechatronics and Machine Dynamics, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Alin Plesa: Department of Mechatronics and Machine Dynamics, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
Marjan Dimec: Fokus Tech d.o.o., 3000 Celje, Slovenia

Sustainability, 2024, vol. 16, issue 7, 1-22

Abstract: 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.

Keywords: rail sustainability; rail safety; autonomous obstacle detection; sub-system; evaluation in real environment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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