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Characterizing the driving style behavior using artificial intelligence techniques

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Characterizing the driving style behavior using artificial intelligence techniques

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dc.contributor.author Meseguer Anastasio, Javier Enrique es_ES
dc.contributor.author Tavares de Araujo Cesariny Calafate, Carlos Miguel es_ES
dc.contributor.author Cano Escribá, Juan Carlos es_ES
dc.contributor.author Manzoni, Pietro es_ES
dc.date.accessioned 2016-07-07T12:59:37Z
dc.date.available 2016-07-07T12:59:37Z
dc.date.issued 2013-10-21
dc.identifier.uri http://hdl.handle.net/10251/67314
dc.description © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works es_ES
dc.description.abstract [EN] The On Board Diagnosis (OBD-II) standard allows accessing the vehicles’ Electronic Control Unit (ECU) easily through a Bluetooth OBD-II connector. This paper presents the DrivingStyles architecture, which adopts data mining techniques and neural networks to analyze and generate a classification of driving styles by analysing the characteristics of the driver along the route followed. The final goal is to assist drivers at correcting the bad habits in their driving behavior, while offering helpful tips to improve fuel economy. Since it is well known that smart driving can lead to a lower fuel consumption, the environmental impact is also reduced. A study involving more than 180 users is being carried out, where their real time traces (with different traffic conditions) is sent periodically to the platform. DrivingStyles is currently available on the Google Play Store platform for free download, and has achieved more than 2800 downloads from different countries in just a few months es_ES
dc.description.sponsorship This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01, and by the Universitat Politècnica de València through the ABATIS project (PAID-05-12).
dc.format.extent 3 es_ES
dc.language Inglés es_ES
dc.publisher IEEE es_ES
dc.relation MICINN/TIN2011-27543- C03-01 es_ES
dc.relation UPV/ABATIS/ PAID-05-12 es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Driving styles es_ES
dc.subject Android smartphone es_ES
dc.subject OBD-II es_ES
dc.subject Neural Networks es_ES
dc.subject Eco-driving es_ES
dc.subject Wireless Network es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Characterizing the driving style behavior using artificial intelligence techniques es_ES
dc.type Comunicación en congreso es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Instrumentación para Imagen Molecular - Institut d'Instrumentació per a Imatge Molecular es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Meseguer Anastasio, JE.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2013). Characterizing the driving style behavior using artificial intelligence techniques. IEEE. http://hdl.handle.net/10251/67314 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.conferencename 38th IEEE Conference on Local Computer Networks (LCN 2013) es_ES
dc.relation.conferencedate October, 21-24, 2013 es_ES
dc.relation.conferenceplace Sydney, Australia es_ES
dc.relation.publisherversion http://www.ieeelcn.org/prior/LCN38/lcn38demos.html es_ES
dc.type.version info:eu repo/semantics/publishedVersion es_ES
dc.relation.senia 257368 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación (MICINN)
dc.contributor.funder Universitat Politècnica de València (UPV)


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