Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/72230
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Costa, Miguel Ângelo Peixoto | por |
dc.contributor.author | Oliveira, Daniel | por |
dc.contributor.author | Pinto, Sandro | por |
dc.contributor.author | Tavares, Adriano | por |
dc.date.accessioned | 2021-04-22T08:52:14Z | - |
dc.date.issued | 2019-10-01 | - |
dc.identifier.citation | Costa, M., et. al. (2019). “Detecting Driver’s Fatigue, Distraction and Activity Using a Non-Intrusive Ai-Based Monitoring System.” Journal of Artificial Intelligence and Soft Computing Research Vol. 9, No. 4, no. 247–66 | por |
dc.identifier.issn | 2083-2567 | - |
dc.identifier.uri | https://hdl.handle.net/1822/72230 | - |
dc.description.abstract | The lack of attention during the driving task is considered as a major risk factor for fatal road accidents around the world. Despite the ever-growing trend for autonomous driving which promises to bring greater road-safety benefits, the fact is today's vehicles still only feature partial and conditional automation, demanding frequent driver action. Moreover, the monotony of such a scenario may induce fatigue or distraction, reducing driver awareness and impairing the regain of the vehicle's control. To address this challenge, we introduce a non-intrusive system to monitor the driver in terms of fatigue, distraction, and activity. The proposed system explores state-of-the-art sensors, as well as machine learning algorithms for data extraction and modeling. In the domain of fatigue supervision, we propose a feature set that considers the vehicle's automation level. In terms of distraction assessment, the contributions concern (i) a holistic system that covers the full range of driver distraction types and (ii) a monitoring unit that predicts the driver activity causing the faulty behavior. By comparing the performance of Support Vector Machines against Decision Trees, conducted experiments indicated that our system can predict the driver's state with an accuracy ranging from 89% to 93%. | por |
dc.description.sponsorship | European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) Project no 002797; Funding Reference: POCI-01- 0247-FEDER-002797 | por |
dc.language.iso | eng | por |
dc.publisher | Sciendo | por |
dc.rights | restrictedAccess | por |
dc.subject | Driver distraction monitoring | por |
dc.subject | Driver fatigue monitoring | por |
dc.subject | Driver monitoring system | por |
dc.subject | Intelligent transportation systems | por |
dc.title | Detecting driver's fatigue, distraction and activity using a non-intrusive AI-based monitoring system | por |
dc.type | article | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://yadda.icm.edu.pl/baztech/element/bwmeta1.element.baztech-382ddc33-a357-4a7e-8aad-55e01c32034b | por |
oaire.citationStartPage | 247 | por |
oaire.citationEndPage | 266 | por |
oaire.citationIssue | 4 | por |
oaire.citationVolume | 9 | por |
dc.date.updated | 2021-04-21T22:22:10Z | - |
dc.identifier.doi | 10.2478/jaiscr-2019-0007 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 10674 | - |
sdum.journal | Journal of Artificial Intelligence and Soft Computing Research | por |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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10.2478_jaiscr-2019-0007.pdf Acesso restrito! | 1,41 MB | Adobe PDF | Ver/Abrir |