Recognition of the Driving Style in Vehicle Drivers

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dc.citation.journalTitleSensorseng
dc.contributor.authorCordero, Jorge
dc.contributor.authorAguilar, Jose
dc.contributor.authorAguilar, Kristell
dc.contributor.authorChavez, Danilo
dc.contributor.authorPuerto, Eduard
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería de Sistemasspa
dc.contributor.researchgroupI+D+I en Tecnologías de la Información y las Comunicacionesspa
dc.creatorCordero, Jorge
dc.creatorAguilar, Jose
dc.creatorAguilar, Kristell
dc.creatorChavez, Danilo
dc.creatorPuerto, Eduard
dc.date.accessioned2021-04-12T20:55:48Z
dc.date.available2021-04-12T20:55:48Z
dc.date.issued2020-05-01
dc.description.abstractThis paper presents three different approaches to recognize driving style based on a hierarchical-model. Specifically, it proposes a hierarchical model for the recognition of the driving style for advanced driver-assistance systems (ADAS) for vehicles. This hierarchical model for the recognition of the style of the car driving considers three aspects: the driver emotions, the driver state, and finally, the driving style itself. In this way, the proposed hierarchical pattern is composed of three levels of descriptors/features, one to recognize the emotional states, another to recognize the driver state, and the last one to recognize the driving style. Each level has a set of descriptors, which can be sensed in a real context. Finally, the paper presents three driving style recognition algorithms based on different paradigms. One is based on fuzzy logic, another is based on chronicles (a temporal logic paradigm), and the last is based on an algorithm that uses the idea of the recognition process of the neocortex, called Ar2p (Algoritmo Recursivo de Reconocimiento de Patrones, for its acronym in Spanish). In the paper, these approaches are compared using real datasets, using different metrics of interest in the context of the Internet of the Things, in order to determine their capabilities of reasoning, adaptation, and the communication of information. In general, the initial results are encouraging, specifically in the cases of chronicles and Ar2p, which give the best results.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=11917
dc.identifier.doi10.3390/s20092597
dc.identifier.issn14248220
dc.identifier.otherWOS;000537106200161
dc.identifier.otherPUBMED;32370223
dc.identifier.otherSCOPUS;2-s2.0-85084183896
dc.identifier.urihttp://hdl.handle.net/10784/28639
dc.language.isoengeng
dc.publisherMDPI AG
dc.relationDOI;10.3390/s20092597
dc.relationWOS;000537106200161
dc.relationPUBMED;32370223
dc.relationSCOPUS;2-s2.0-85084183896
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85084183896&doi=10.3390%2fs20092597&partnerID=40&md5=d2361e8a5648f40320746328a3bf7177
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1424-8220
dc.sourceSensors
dc.subjectpattern recognitioneng
dc.subjectdriving styleeng
dc.subjectintelligent techniqueseng
dc.subjectadvanced driver-assistance systemseng
dc.titleRecognition of the Driving Style in Vehicle Driverseng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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