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

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Rahman, Hamidur,Doctoral Student,1984-Mälardalens högskola,Inbyggda system (author)

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

Article/chapterEnglish2021

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2021-11-30
MDPI,2021
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LIBRIS-ID:oai:DiVA.org:mdh-56825
https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-56825URI
https://doi.org/10.3390/s21238019DOI

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Language:English
Summary in:English

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Subject category:art swepub-publicationtype

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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.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Datorsystem hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems hsv//eng
Cognitive load
Eye-movement
Machine learning
Non-contact

Added entries (persons, corporate bodies, meetings, titles ...)

Ahmed, Mobyen Uddin,Dr,1976-Mälardalens högskola,Inbyggda system(Swepub:mdh)mad02 (author)
Barua, ShaibalMälardalens högskola,Inbyggda system(Swepub:mdh)sba01 (author)
Funk, Peter,1957-Mälardalens högskola,Inbyggda system(Swepub:mdh)pfk01 (author)
Begum, Shahina,1977-Mälardalens högskola,Inbyggda system(Swepub:mdh)sbm02 (author)
Mälardalens högskolaInbyggda system (creator_code:org_t)

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In:Sensors: MDPI21:231424-8220

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