Multimodal Features for Detection of Driver Stress and Fatigue: Review

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dc.contributor.authorNěmcová, Andreacs
dc.contributor.authorSvozilová, Veronikacs
dc.contributor.authorBucsuházy, Kateřinacs
dc.contributor.authorSmíšek, Radovancs
dc.contributor.authorMézl, Martincs
dc.contributor.authorHesko, Branislavcs
dc.contributor.authorBelák, Michalcs
dc.contributor.authorBilík, Martincs
dc.contributor.authorMaxera, Pavelcs
dc.contributor.authorSeitl, Martincs
dc.contributor.authorDominik, Tomášcs
dc.contributor.authorSemela, Marekcs
dc.contributor.authorŠucha, Matúšcs
dc.contributor.authorKolář, Radimcs
dc.coverage.issue6cs
dc.coverage.volume22cs
dc.date.accessioned2020-11-19T15:54:44Z
dc.date.available2020-11-19T15:54:44Z
dc.date.issued2021-06-01cs
dc.description.abstractDriver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios.en
dc.formattextcs
dc.format.extent3214-3233cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS. 2021, vol. 22, issue 6, p. 3214-3233.en
dc.identifier.doi10.1109/TITS.2020.2977762cs
dc.identifier.issn1558-0016cs
dc.identifier.other163233cs
dc.identifier.urihttp://hdl.handle.net/11012/195664
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMScs
dc.relation.urihttps://ieeexplore.ieee.org/document/9031734cs
dc.rights(C) IEEEcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1558-0016/cs
dc.subjectdriver fatigueen
dc.subjectdriver stressen
dc.subjecttraffic accidenten
dc.subjectphysiological signalsen
dc.subjectmultimodal featuresen
dc.titleMultimodal Features for Detection of Driver Stress and Fatigue: Reviewen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionacceptedVersionen
sync.item.dbidVAV-163233en
sync.item.dbtypeVAVen
sync.item.insts2021.09.02 08:53:09en
sync.item.modts2021.09.02 08:14:28en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
thesis.grantorVysoké učení technické v Brně. Ústav soudního inženýrství. Odbor znalectví ve strojírenství, analýza dopravních nehod a oceňování motorových vozidelcs
thesis.grantorVysoké učení technické v Brně. . Univerzita Palackého v Olomoucics
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Odbor znalectví ve strojírenství, analýza dopravních nehod a oceňování motorových vozidel
Ústav biomedicínského inženýrství