AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.21/14081 |
Resumo: | As 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. |
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AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhiclesBiometricsBiosignalsComputer visionDataDriverDrowsinessSimulatorVehiclesAs 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.IEEERCIPLEsteves, TelmaPinto, João RibeiroFerreira, Pedro M.Costa, Pedro AmaroRodrigues, Lourenço AbrunhosaAntunes, InêsLopes, Gabrielgamito, pedroAbrantes, ArnaldoJorge, PedroLourenço, AndréSequeira, Ana F.Cardoso, Jaime S.Rebelo, Ana2021-12-21T20:09:12Z2021-11-122021-11-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/14081engESTEVES, 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- 153700.10.1109/ACCESS.2021.31280162169-3536info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-03T10:09:46Zoai:repositorio.ipl.pt:10400.21/14081Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:21:57.100829Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles |
title |
AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles |
spellingShingle |
AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles Esteves, Telma Biometrics Biosignals Computer vision Data Driver Drowsiness Simulator Vehicles |
title_short |
AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles |
title_full |
AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles |
title_fullStr |
AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles |
title_full_unstemmed |
AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles |
title_sort |
AUTOMOTIVE: a case study on AUTOmatic multiMOdal drowsiness detecTIon for smart VEhicles |
author |
Esteves, Telma |
author_facet |
Esteves, Telma Pinto, João Ribeiro Ferreira, Pedro M. Costa, Pedro Amaro Rodrigues, Lourenço Abrunhosa Antunes, Inês Lopes, Gabriel gamito, pedro Abrantes, Arnaldo Jorge, Pedro Lourenço, André Sequeira, Ana F. Cardoso, Jaime S. Rebelo, Ana |
author_role |
author |
author2 |
Pinto, João Ribeiro Ferreira, Pedro M. Costa, Pedro Amaro Rodrigues, Lourenço Abrunhosa Antunes, Inês Lopes, Gabriel gamito, pedro Abrantes, Arnaldo Jorge, Pedro Lourenço, André Sequeira, Ana F. Cardoso, Jaime S. Rebelo, Ana |
author2_role |
author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
Esteves, Telma Pinto, João Ribeiro Ferreira, Pedro M. Costa, Pedro Amaro Rodrigues, Lourenço Abrunhosa Antunes, Inês Lopes, Gabriel gamito, pedro Abrantes, Arnaldo Jorge, Pedro Lourenço, André Sequeira, Ana F. Cardoso, Jaime S. Rebelo, Ana |
dc.subject.por.fl_str_mv |
Biometrics Biosignals Computer vision Data Driver Drowsiness Simulator Vehicles |
topic |
Biometrics Biosignals Computer vision Data Driver Drowsiness Simulator Vehicles |
description |
As 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-21T20:09:12Z 2021-11-12 2021-11-12T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.21/14081 |
url |
http://hdl.handle.net/10400.21/14081 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ESTEVES, 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. 10.1109/ACCESS.2021.3128016 2169-3536 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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