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dc.contributor.authorEsmorís Pena, Alberto Manuel
dc.contributor.authorLópez Vilariño, David
dc.contributor.authorFernández Arango, David
dc.contributor.authorVarela-García, Francisco-Alberto
dc.contributor.authorCabaleiro, José Carlos
dc.contributor.authorRivera, Francisco F.
dc.date.accessioned2024-04-19T19:30:17Z
dc.date.available2024-04-19T19:30:17Z
dc.date.issued2023
dc.identifier.citationEsmorís, A. M., Vilariño, D. L., Arango, D. F., Varela‐García, F. A., Cabaleiro, J. C., Rivera, F. F. (2023). Characterizing zebra crossing zones using LiDAR data. Computer‐Aided Civil and Infrastructure Engineering, 38(13), 1767-1788. https://doi.org/10.1111/mice.12968es_ES
dc.identifier.urihttp://hdl.handle.net/2183/36272
dc.description.abstract[Abstract:] Light detection and ranging (LiDAR) scanning in urban environments leads to accurate and dense three-dimensional point clouds where the different elements in the scene can be precisely characterized. In this paper, two LiDAR-based algorithms that complement each other are proposed. The first one is a novel profiling method robust to noise and obstacles. It accurately characterizes the curvature, the slope, the height of the sidewalks, obstacles, and defects such as potholes. It was effective for 48 of 49 detected zebra crossings, even in the presence of pedestrians or vehicles in the crossing zone. The second one is a detailed quantitative summary of the state of the zebra crossing. It contains information about the location, the geometry, and the road marking. Coarse grain statistics are more prone to obstacle-related errors and are only fully reliable for 18 zebra crossings free from significant obstacles. However, all the anomalous statistics can be analyzed by looking at the associated profiles. The results can help in the maintenance of urban roads. More specifically, they can be used to improve the quality and safety of pedestrian routes.es_ES
dc.description.sponsorshipThis work has received financial support from the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2019-2022 ED431G-2019/04, reference competitive group 2019-2021, ED431C 2018/19 and the predoctoral grant of Alberto Esmorís Ref. ED481A-2020/231) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System. This work was also supported by the Ministry of Economy and Competitiveness, Government of Spain (Grant Number PID2019-104834GB-I00) and the National Department of Traffic (DGT) through the project Analysis of Indicators Big-Geodata on Urban Roads for the Dynamic Design of Safe School Roads (Grant Number SPIP2017-02340).es_ES
dc.description.sponsorshipXunta de Galicia; ED431G-2019/04es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2018/19es_ES
dc.description.sponsorshipXunta de Galicia; ED481A-2020/231es_ES
dc.description.sponsorshipDirección General de Tráfico (DGT); SPIP2017-02340es_ES
dc.language.isoenges_ES
dc.publisherWileyes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104834GB-I00/ES/COMPUTACION DE ALTAS PRESTACIONES Y CLOUD PARA APLICACIONES DE ALTO INTERESes_ES
dc.relation.urihttps://doi.org/10.1111/mice.12968es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectLight detection and ranginges_ES
dc.subjectLiDARes_ES
dc.subjectZebra crossingses_ES
dc.subjectUrban roadses_ES
dc.subjectQuality pedestrian routeses_ES
dc.subjectSafe pedestrian routeses_ES
dc.titleCharacterizing zebra crossing zones using LiDAR dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleComputer‐Aided Civil and Infrastructure Engineeringes_ES
UDC.volume38es_ES
UDC.issue13es_ES
UDC.startPage1767es_ES
UDC.endPage1788es_ES
dc.identifier.doi10.1111/mice.12968


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