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AUTOMOTIVE

2021Artigo científico Acesso aberto
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dc.contributor.authorEsteves, Telma
dc.contributor.authorPinto, João Ribeiro
dc.contributor.authorFerreira, Pedro M.
dc.contributor.authorCosta, Pedro Amaro
dc.contributor.authorRodrigues, Lourenço Abrunhosa
dc.contributor.authorAntunes, Inês
dc.contributor.authorLopes, Gabriel
dc.contributor.authorGamito, Pedro
dc.contributor.authorAbrantes, Arnaldo J.
dc.contributor.authorJorge, Pedro M.
dc.contributor.authorLourenço, Andre
dc.contributor.authorSequeira, Ana F.
dc.contributor.authorCardoso, Jaime S.
dc.contributor.authorRebelo, Ana
dc.contributor.institutionDF – Departamento de Física
dc.contributor.pblInstitute of Electrical and Electronics Engineers (IEEE)
dc.date.accessioned2022-07-28T22:24:35Z
dc.date.available2022-07-28T22:24:35Z
dc.date.issued2021
dc.descriptionPublisher Copyright: © 2013 IEEE.
dc.description.abstractAs technology and artificial intelligence conquer a place under the spotlight in the automotive world, driver drowsiness monitoring systems have sparked much interest as a way to increase safety and avoid sleepiness-related accidents. Such technologies, however, stumble upon the observation that each driver presents a distinct set of behavioral and physiological manifestations of drowsiness, thus rendering its objective assessment a non-trivial process. The AUTOMOTIVE project studied the application of signal processing and machine learning techniques for driver-specific drowsiness detection in smart vehicles, enabled by immersive driving simulators. More broadly, comprehensive research on biometrics using the electrocardiogram (ECG) and face enables the continuous learning of subject-specific models of drowsiness for more efficient monitoring. This paper aims to offer a holistic but comprehensive view of the research and development work conducted for the AUTOMOTIVE project across the various addressed topics and how it ultimately brings us closer to the target of improved driver drowsiness monitoring.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent23
dc.format.extent4216370
dc.identifier.doi10.1109/ACCESS.2021.3128016
dc.identifier.issn2169-3536
dc.identifier.otherPURE: 45662740
dc.identifier.otherPURE UUID: ef88596d-86b4-49a8-b158-0dd6c81582dc
dc.identifier.otherScopus: 85119435181
dc.identifier.otherWOS: 000721993000001
dc.identifier.urihttp://hdl.handle.net/10362/142597
dc.identifier.urlhttps://www.scopus.com/pages/publications/85119435181
dc.language.isoeng
dc.peerreviewedyes
dc.subjectBiometrics
dc.subjectbiosignals
dc.subjectcomputer vision
dc.subjectdata
dc.subjectdriver
dc.subjectdrowsiness
dc.subjectsimulator
dc.subjectvehicles
dc.subjectGeneral Computer Science
dc.subjectGeneral Materials Science
dc.subjectGeneral Engineering
dc.subjectElectrical and Electronic Engineering
dc.titleAUTOMOTIVEen
dc.title.subtitleA Case Study on AUTOmatic multiMOdal Drowsiness detecTIon for smart VEhiclesen
dc.typejournal article
degois.publication.firstPage153678
degois.publication.lastPage153700
degois.publication.titleIEEE Access
degois.publication.volume9
dspace.entity.typePublication
rcaap.rightsopenAccess

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