Vision-based driver’s cognitive load classification considering eye movement using machine learning and deep learning

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Fältnamn	Indikatorer	Metadata
000		03476naa a2200409 4500
001		oai:DiVA.org:mdh-56825
003		SwePub
008		211223s2021 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-568252 URI
024	7	a https://doi.org/10.3390/s212380192 DOI
040		a (SwePub)mdh
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Rahman, Hamidur,c Doctoral Student,d 1984-u Mälardalens högskola,Inbyggda system4 aut0 (Swepub:mdh)rhr01
245	1 0	a Vision-based driver’s cognitive load classification considering eye movement using machine learning and deep learning
264		c 2021-11-30
264	1	b MDPI,c 2021
338		a print2 rdacarrier
520		a Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drivers’ unsafe behaviors. Therefore, to make the traffic environment safe it is important to keep the driver alert and awake both in human and autonomous driving cars. A driver’s cognitive load is considered a good indication of alertness, but determining cognitive load is challenging and the acceptance of wire sensor solutions are not preferred in real-world driving scenarios. The recent development of a non-contact approach through image processing and decreasing hardware prices enables new solutions and there are several interesting features related to the driver’s eyes that are currently explored in research. This paper presents a vision-based method to extract useful parameters from a driver’s eye movement signals and manual feature extraction based on domain knowledge, as well as automatic feature extraction using deep learning architectures. Five machine learning models and three deep learning architectures are developed to classify a driver’s cognitive load. The results show that the highest classification accuracy achieved is 92% by the support vector machine model with linear kernel function and 91% by the convolutional neural networks model. This non-contact technology can be a potential contributor in advanced driver assistive systems.
650	7	a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Datorsystem0 (SwePub)202062 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Computer Systems0 (SwePub)202062 hsv//eng
653		a Cognitive load
653		a Eye-movement
653		a Machine learning
653		a Non-contact
700	1	a Ahmed, Mobyen Uddin,c Dr,d 1976-u Mälardalens högskola,Inbyggda system4 aut0 (Swepub:mdh)mad02
700	1	a Barua, Shaibalu Mälardalens högskola,Inbyggda system4 aut0 (Swepub:mdh)sba01
700	1	a Funk, Peter,d 1957-u Mälardalens högskola,Inbyggda system4 aut0 (Swepub:mdh)pfk01
700	1	a Begum, Shahina,d 1977-u Mälardalens högskola,Inbyggda system4 aut0 (Swepub:mdh)sbm02
710	2	a Mälardalens högskolab Inbyggda system4 org
773	0	t Sensorsd : MDPIg 21:23q 21:23x 1424-8220
856	4	u https://doi.org/10.3390/s21238019y Fulltext
856	4	u https://www.mdpi.com/1424-8220/21/23/8019/pdf
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-56825
856	4 8	u https://doi.org/10.3390/s21238019

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Av författaren/redakt...: Rahman, Hamidur, ...; Ahmed, Mobyen Ud ...; Barua, Shaibal; Funk, Peter, 195 ...; Begum, Shahina, ...

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Datorsystem

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Av lärosätet: Mälardalens universitet

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