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AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles

2021-11-12Journal article Open access
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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
dc.contributor.authorJorge, Pedro
dc.contributor.authorLourenço, André
dc.contributor.authorSequeira, Ana F.
dc.contributor.authorCardoso, Jaime S.
dc.contributor.authorRebelo, Ana
dc.date.accessioned2021-12-21T20:09:12Z
dc.date.available2021-12-21T20:09:12Z
dc.date.issued2021-11-12
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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationESTEVES, Telma; [et al] – AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles. IEEE Access. eISSN 2169-3536. Vol. 9 (2021), pp. 153678- 153700.pt_PT
dc.identifier.doi10.1109/ACCESS.2021.3128016pt_PT
dc.identifier.eissn2169-3536
dc.identifier.urihttp://hdl.handle.net/10400.21/14081
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9614134&tag=1pt_PT
dc.subjectBiometricspt_PT
dc.subjectBiosignalspt_PT
dc.subjectComputer visionpt_PT
dc.subjectDatapt_PT
dc.subjectDriverpt_PT
dc.subjectDrowsinesspt_PT
dc.subjectSimulatorpt_PT
dc.subjectVehiclespt_PT
dc.titleAUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhiclespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage153700pt_PT
oaire.citation.startPage153678pt_PT
oaire.citation.titleIEEE Accesspt_PT
oaire.citation.volume9pt_PT
person.familyNameRibeiro Pinto
person.familyNamegamito
person.familyNameAbrantes
person.givenNameJoão Tiago
person.givenNamepedro
person.givenNameArnaldo
person.identifierhhF9Q8kAAAAJ
person.identifier.ciencia-id5F13-BBF3-1A46
person.identifier.ciencia-id4C10-9E13-300B
person.identifier.ciencia-id7B1E-D0E8-5319
person.identifier.ciencia-idF61C-F1EB-88B8
person.identifier.orcid0000-0003-4956-5902
person.identifier.orcid0000-0003-0585-8447
person.identifier.orcid0000-0002-9911-6833
person.identifier.orcid0000-0003-1597-410X
person.identifier.scopus-author-id57195383702
person.identifier.scopus-author-id7003329558
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication1243547e-c844-4024-a62a-0b9b83e6ae0d
relation.isAuthorOfPublicatione41ff7df-50ee-4b01-b2b0-1f4f1bf4f037
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relation.isAuthorOfPublicationaaf73613-1657-426b-bfad-61538719c7a1
relation.isAuthorOfPublication.latestForDiscovery7fb56aaa-cf87-4ce3-8047-b07e2a8bc758

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