AUTOMOTIVE: A Case Study on AUTOmatic multiMOdal Drowsiness detecTIon for smart VEhicles

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 eac...

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Autores principales:	Telma Esteves, Joao Ribeiro Pinto, Pedro M. Ferreira, Pedro Amaro Costa, Lourenco Abrunhosa Rodrigues, Ines Antunes, Gabriel Lopes, Pedro Gamito, Arnaldo J. Abrantes, Pedro M. Jorge, Andre Lourenco, Ana F. Sequeira, Jaime S. Cardoso, Ana Rebelo
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Biometrics biosignals computer vision data driver drowsiness Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/9c060e043ea3414fb915dc5e1cded527
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spelling	oai:doaj.org-article:9c060e043ea3414fb915dc5e1cded5272021-11-24T00:02:44ZAUTOMOTIVE: A Case Study on AUTOmatic multiMOdal Drowsiness detecTIon for smart VEhicles2169-353610.1109/ACCESS.2021.3128016https://doaj.org/article/9c060e043ea3414fb915dc5e1cded5272021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9614134/https://doaj.org/toc/2169-3536As 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.Telma EstevesJoao Ribeiro PintoPedro M. FerreiraPedro Amaro CostaLourenco Abrunhosa RodriguesInes AntunesGabriel LopesPedro GamitoArnaldo J. AbrantesPedro M. JorgeAndre LourencoAna F. SequeiraJaime S. CardosoAna RebeloIEEEarticleBiometricsbiosignalscomputer visiondatadriverdrowsinessElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 153678-153700 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biometrics biosignals computer vision data driver drowsiness Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Biometrics biosignals computer vision data driver drowsiness Electrical engineering. Electronics. Nuclear engineering TK1-9971 Telma Esteves Joao Ribeiro Pinto Pedro M. Ferreira Pedro Amaro Costa Lourenco Abrunhosa Rodrigues Ines Antunes Gabriel Lopes Pedro Gamito Arnaldo J. Abrantes Pedro M. Jorge Andre Lourenco Ana F. Sequeira Jaime S. Cardoso Ana Rebelo AUTOMOTIVE: A Case Study on AUTOmatic multiMOdal Drowsiness detecTIon for smart 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.
format	article
author	Telma Esteves Joao Ribeiro Pinto Pedro M. Ferreira Pedro Amaro Costa Lourenco Abrunhosa Rodrigues Ines Antunes Gabriel Lopes Pedro Gamito Arnaldo J. Abrantes Pedro M. Jorge Andre Lourenco Ana F. Sequeira Jaime S. Cardoso Ana Rebelo
author_facet	Telma Esteves Joao Ribeiro Pinto Pedro M. Ferreira Pedro Amaro Costa Lourenco Abrunhosa Rodrigues Ines Antunes Gabriel Lopes Pedro Gamito Arnaldo J. Abrantes Pedro M. Jorge Andre Lourenco Ana F. Sequeira Jaime S. Cardoso Ana Rebelo
author_sort	Telma Esteves
title	AUTOMOTIVE: A Case Study on AUTOmatic multiMOdal Drowsiness detecTIon for smart 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
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/9c060e043ea3414fb915dc5e1cded527
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AUTOMOTIVE: A Case Study on AUTOmatic multiMOdal Drowsiness detecTIon for smart VEhicles

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