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

Detalhes bibliográficos
Autor(a) principal: Esteves, Telma
Data de Publicação: 2021
Outros Autores: 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
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.
id RCAP_6820cf5232151906709bc12e10fae929
oai_identifier_str oai:repositorio.ipl.pt:10400.21/14081
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling 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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution 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
repository.mail.fl_str_mv
_version_ 1799133490921340928