Publication: Toward safer highways: predicting driver stress in varying conditions on habitual routes
dc.affiliation.dpto | UC3M. Departamento de Ingeniería Telemática | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST) | es |
dc.contributor.author | Corcoba Magaña, Víctor | es |
dc.contributor.author | Muñoz Organero, Mario | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | es |
dc.contributor.funder | Ministerio de Educación, Cultura y Deporte (España) | es |
dc.date.accessioned | 2021-02-04T12:58:27Z | |
dc.date.available | 2021-02-04T12:58:27Z | |
dc.date.issued | 2017-12-01 | |
dc.description.abstract | Driver stress is a growing problem in the transportation industry. It causes a deterioration of cognitive skills, resulting in poor driving and an increase in the likelihood of traffic accidents. Prediction models allow us to avoid or at least minimize the negative consequences of stress. In this article, an algorithm based on deep learning is proposed to predict driver stress. This type of algorithm detects complex relationships among variables. At the same time, it avoids overfitting. The prediction of the upcoming stress level is made by taking into account driving behavior (acceleration, deceleration, speed) and the previous stress level. | en |
dc.description.sponsorship | This work was supported by the “HERMES-SMART DRIVER” project TIN2013-46801-C4-2-R, funded by the Spanish Ministry of Economy, Industry and Competitiveness, from the grant PRX15/00036 from the Ministerio de Educación Cultura y Deporte, and from a sabbatical leave by the Carlos III University of Madrid. | en |
dc.description.status | Publicado | es |
dc.format.extent | 7 | |
dc.identifier.bibliographicCitation | IEEE Vehicular Technology Magazine, (2017), 12(4), pp.: 69 - 76. | en |
dc.identifier.doi | https://doi.org/10.1109/MVT.2017.2692059 | |
dc.identifier.issn | 1556-6072 | |
dc.identifier.publicationfirstpage | 69 | |
dc.identifier.publicationissue | 4 | |
dc.identifier.publicationlastpage | 76 | |
dc.identifier.publicationtitle | IEEE Vehicular Technology Magazine | en |
dc.identifier.publicationvolume | 12 | |
dc.identifier.uri | https://hdl.handle.net/10016/31863 | |
dc.identifier.uxxi | AR/0000026369 | |
dc.language.iso | eng | en |
dc.publisher | IEEE | en |
dc.relation.projectID | Gobierno de España. TIN2013-46801-C4-2-R | es |
dc.relation.projectID | Gobierno de España. PRX15/00036 | es |
dc.rights | © 2017 IEEE. | en |
dc.rights.accessRights | open access | en |
dc.subject.eciencia | Telecomunicaciones | es |
dc.subject.other | Prediction algorithms | en |
dc.subject.other | Stress measurement | en |
dc.subject.other | Heart rate variability | en |
dc.subject.other | Acceleration | en |
dc.subject.other | Vehicle safety | en |
dc.subject.other | Human factors | en |
dc.title | Toward safer highways: predicting driver stress in varying conditions on habitual routes | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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