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dc.contributor.authorCordero, Jorge
dc.contributor.authorAguilar, Kristell
dc.contributor.authorChávez, Danilo
dc.contributor.authorPuerto, Eduard
dc.contributor.authorAguilar, Jose
dc.date.accessioned2021-11-30T16:59:21Z
dc.date.available2021-11-30T16:59:21Z
dc.date.issued2020-05-02
dc.identifier.urihttp://repositorio.ufps.edu.co/handle/ufps/1576
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.format.extent28 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherSensorsspa
dc.relation.ispartofSensors
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).eng
dc.sourcehttps://www.mdpi.com/1424-8220/20/9/2597spa
dc.titleRecognition of the driving style in vehicle driverseng
dc.typeArtículo de revistaspa
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dc.identifier.doihttps://doi.org/10.3390/s20092597
dc.publisher.placeSuizaspa
dc.relation.citationeditionVol.20 No.9.(2020)spa
dc.relation.citationendpage28spa
dc.relation.citationissue9(2020)spa
dc.relation.citationstartpage1spa
dc.relation.citationvolume20spa
dc.relation.citesCordero, J., Aguilar, J., Aguilar, K., Chávez, D., & Puerto, E. (2020). Recognition of the driving style in vehicle drivers. Sensors, 20(9), 2597.
dc.relation.ispartofjournalSensorsspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.subject.proposalpattern recognitioneng
dc.subject.proposaldriving styleeng
dc.subject.proposalintelligent techniqueseng
dc.subject.proposaladvanced driver-assistance systemseng
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa


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