{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:10:24Z","timestamp":1777129824996,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/50022\/2020"],"award-info":[{"award-number":["UIDB\/50022\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["46111"],"award-info":[{"award-number":["46111"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007434","name":"ANI-Ag\u00eancia Nacional de Inova\u00e7\u00e3o, S.A.","doi-asserted-by":"publisher","award":["UIDB\/50022\/2020"],"award-info":[{"award-number":["UIDB\/50022\/2020"]}],"id":[{"id":"10.13039\/501100007434","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007434","name":"ANI-Ag\u00eancia Nacional de Inova\u00e7\u00e3o, S.A.","doi-asserted-by":"publisher","award":["46111"],"award-info":[{"award-number":["46111"]}],"id":[{"id":"10.13039\/501100007434","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Rail travel is one of the safest means of transportation, with increasing usage in recent years. One of the major safety concerns in the railway relates to intrusions. Therefore, the timely detection of obstacles is crucial for ensuring operational safety. This is a complex problem with multiple contributing factors, from environmental to psychological. While machine learning (ML) has proven effective in related applications, such as autonomous road-based driving, the railway sector faces unique challenges due to limited image data availability and difficult data acquisition, hindering the applicability of conventional ML methods. To mitigate this, the present study proposes a novel framework leveraging LiDAR technology (Light Detection and Ranging) and previous knowledge to address these data scarcity limitations and enhance obstacle detection capabilities on railways. The proposed framework combines the strengths of long-range LiDAR (capable of detecting obstacles up to 500 m away) and GNSS data, which results in precise coordinates that accurately describe the train\u2019s position relative to any obstacles. Using a data fusion approach, pre-existing knowledge about the track topography is incorporated into the LiDAR data processing pipeline in conjunction with the DBSCAN clustering algorithm to identify and classify potential obstacles based on point cloud density patterns. This step effectively segregates potential obstacles from background noise and track structures. The proposed framework was tested within the operational environment of a CP 2600-2620 series locomotive in a short section of the Contumil-Leix\u00f5es line. This real-world testing scenario allowed the evaluation of the framework\u2019s effectiveness under realistic operating conditions. The unique advantages of this approach relate to its effectiveness in tackling data scarcity, which is often an issue for other methods, in a way that enhances obstacle detection in railway operations and may lead to significant improvements in safety and operational efficiency within railway networks.<\/jats:p>","DOI":"10.3390\/app15063118","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T06:53:00Z","timestamp":1741848780000},"page":"3118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Deterministic Light Detection and Ranging (LiDAR)-Based Obstacle Detection in Railways Using Data Fusion"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8631-6529","authenticated-orcid":false,"given":"Susana","family":"Dias","sequence":"first","affiliation":[{"name":"INEGI, Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Rua Dr. Roberto Frias N\u00ba 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4520-3891","authenticated-orcid":false,"given":"Pedro J. S. C. P.","family":"Sousa","sequence":"additional","affiliation":[{"name":"INEGI, Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Rua Dr. Roberto Frias N\u00ba 400, 4200-465 Porto, Portugal"},{"name":"Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0964-3838","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Nunes","sequence":"additional","affiliation":[{"name":"INEGI, Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Rua Dr. Roberto Frias N\u00ba 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3286-7405","authenticated-orcid":false,"given":"Francisco","family":"Afonso","sequence":"additional","affiliation":[{"name":"INEGI, Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Rua Dr. Roberto Frias N\u00ba 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9957-9486","authenticated-orcid":false,"given":"Nuno","family":"Viriato","sequence":"additional","affiliation":[{"name":"INEGI, Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Rua Dr. Roberto Frias N\u00ba 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6592-3063","authenticated-orcid":false,"given":"Paulo J.","family":"Tavares","sequence":"additional","affiliation":[{"name":"INEGI, Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Rua Dr. Roberto Frias N\u00ba 400, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1127-2525","authenticated-orcid":false,"given":"Pedro M. G. P.","family":"Moreira","sequence":"additional","affiliation":[{"name":"INEGI, Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Rua Dr. Roberto Frias N\u00ba 400, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"ref_1","unstructured":"(2024, December 13). Railway Safety Statistics in the EU. Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php?title=Railway_safety_statistics_in_the_EU."},{"key":"ref_2","unstructured":"(2024, December 13). Railway Passenger Transport Statistics. Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php?title=Railway_passenger_transport_statistics_-_quarterly_and_annual_data."},{"key":"ref_3","unstructured":"(2024, December 13). Length of Railway Lines by Number of Tracks and Electrification of Lines. 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