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Fusion of physiological signals for modeling driver awareness levels in conditional autonomous vehicles using semi-supervised learning

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dc.contributor.authorFernandez Matellan, Raul
dc.contributor.authorPuertas Ramírez, David
dc.contributor.authorMartín Gómez, David
dc.contributor.authorGonzález Boticario, Jesús
dc.coverage.spatialVenecia, Italia
dc.coverage.temporal2024-07-08
dc.date.accessioned2025-09-15T11:10:36Z
dc.date.available2025-09-15T11:10:36Z
dc.date.issued2024-10-11
dc.descriptionThe registered version of this conference paper, first published in "2024 27th International Conference on Information Fusion (FUSION), Venice, Italy, 2024, pp. 1-8", is available online at the publisher's website: https://doi.org/10.23919/FUSION59988.2024.10706517
dc.description.abstractThe evolution of autonomous vehicles (AVs) requires a paradigm shift towards the integration of human factors to improve safety and efficiency at levels 2,3 and 4 of automation. This paper presents a comparison of three different fusion technologies (Low-Level fusion, Medium-Level fusion, and a hybrid fusion), highlighting the critical role of multimodal data integration and semi-supervised learning in predicting and adapting to levels of driver awareness. Our approach uses semi-supervised learning to deal with the data labelling problem, using unlabelled data to train an autoencoder and sparsely labelled data to train a 4-state classifier. Our model facilitates the fusion of data from different physiological signals, including skin electrodermal activity, heart rate, body temperature and acceleration. Using real driving data, the Medium-Level fusion approach gives the best performance, achieving 84% accuracy in predicting situations where the user may not be aware enough to take control of the vehicle. This research highlights the essential nature of fusion technologies to create adaptive and user-centred AV systems.en
dc.description.versionversión publicada
dc.identifier.citationR. Fernandez-Matellan, D. Puertas-Ramirez, D. M. Gomez and J. G. Boticario, "Fusion of Physiological Signals for Modeling Driver Awareness Levels in Conditional Autonomous Vehicles using Semi-Supervised Learning," 2024 27th International Conference on Information Fusion (FUSION), Venice, Italy, 2024, pp. 1-8, doi: 10.23919/FUSION59988.2024.10706517
dc.identifier.doihttps://doi.org/10.23919/FUSION59988.2024.10706517
dc.identifier.isbn9781737749769
dc.identifier.urihttps://hdl.handle.net/20.500.14468/30067
dc.language.isoen
dc.publisherIEEE
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.congressFUSION 2024 - 27th International Conference on Information Fusion
dc.relation.departmentInteligencia Artificial
dc.relation.projectidnfo:eu-repo/grantAgreement/AEI/Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digital/TED2021-129485B-C41/ES/SISTEMAS DINAMICOS INTELIGENTES ASISTIDOS CENTRADOS EN EL HUMANO CON TECNOLOGIAS DE SENSADO (HUMANAID-SENS)
dc.relation.projectidnfo:eu-repo/grantAgreement/AEI/Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digital/TED2021-129485B-C44/ES/HUManAID-AVs - SISTEMAS DINÁMICOS INTELIGENTES ASISTIDOS CENTRADOS EN HUMANOS PARA VEHÍCULOS AUTÓNOMOS
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject3304 Tecnología de los ordenadores
dc.titleFusion of physiological signals for modeling driver awareness levels in conditional autonomous vehicles using semi-supervised learningen
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
relation.isAuthorOfPublication749c1f54-02e4-48fb-95f3-c77150d49d37
relation.isAuthorOfPublicatione067a1f1-6036-4974-a582-85b556587d18
relation.isAuthorOfPublication.latestForDiscovery749c1f54-02e4-48fb-95f3-c77150d49d37
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