{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:06:05Z","timestamp":1760058365406,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund within the Operational Programme \u201cBulgarian national recovery plan\u201d, procedure for direct provision of grants \u201cEstablishing of a network of research higher education institutions in Bulgaria\u201d, and the project BG-RRP 2.00","award":["\/"],"award-info":[{"award-number":["\/"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>For reliable autonomous train operation, detecting and classifying obstacles on or near rail tracks, and accurately estimating the distance to these obstacles, is essential. This task is more challenging in low-light conditions, common for freight trains that operate primarily at night. This paper proposes a novel method, FuzzyH, for estimating the distance between a thermal camera and detected obstacles using image-plane homography. By leveraging the homography between the image and rail track planes, and incorporating a fuzzy logic system, the method improves distance estimation accuracy and eliminates the need for complex calibration. This paper also explores the symmetry and asymmetry of fuzzy membership functions and rules. The system was validated on Serbian railways under simulated real-world conditions, demonstrating reliable performance. A key contribution of this method is the use of fuzzy membership functions tailored to specific distance ranges, based on experimental data and domain knowledge, such as regulatory braking distances. This approach improves over traditional methods by offering reliable distance estimates in low-light environments and simplifying the calibration process, ultimately enhancing system accuracy and robustness.<\/jats:p>","DOI":"10.3390\/sym17040509","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:06:35Z","timestamp":1743141995000},"page":"509","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FuzzyH Method for Distance Estimation in Autonomous Train Operation"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0430-8937","authenticated-orcid":false,"given":"Ivan","family":"\u0106iri\u0107","sequence":"first","affiliation":[{"name":"Faculty of Mechanical Engineering, University of Ni\u0161, Aleksandra Medvedeva 14, 18000 Ni\u0161, Serbia"}]},{"given":"Milan","family":"Pavlovi\u0107","sequence":"additional","affiliation":[{"name":"Academy of Technical and Preschool Studies Ni\u0161, Beogradska 18, 18000 Ni\u0161, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9811-6615","authenticated-orcid":false,"given":"Danijela","family":"Risti\u0107-Durrant","sequence":"additional","affiliation":[{"name":"OHB Digital Services GmbH, Konrad-Zuse-Str. 8, 28359 Bremen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8584-5958","authenticated-orcid":false,"given":"Lubomir","family":"Dimitrov","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Technical University Sofia, Bul. Sv. Kliment Ohridski 8, 1756 Sofia, Bulgaria"}]},{"given":"Vlastimir","family":"Nikoli\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, University of Ni\u0161, Aleksandra Medvedeva 14, 18000 Ni\u0161, Serbia"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"ref_1","unstructured":"Song, M., Stevi\u0107, \u017d., Badi, I., Marinkovi\u0107, D., Lv, Y., and Zhong, K. (2024). Assessing Public Acceptance of Autonomous Vehicles Using a Novel IRN Piprecia\u2014IRN Aroman Model. Facta Univ. Ser. Mech. Eng., 1\u201320."},{"key":"ref_2","first-page":"611","article-title":"AI Powered Obstacle Distance Estimation for Onboard Autonomous Train Operation","volume":"29","year":"2022","journal-title":"Teh. Vjesn. Tech. Gaz."},{"key":"ref_3","first-page":"95","article-title":"Distance Estimation Model for Thermal Vision Systems Using Gaussian Process Regression","volume":"2","year":"2023","journal-title":"Innov. Mech. 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