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Vision-Based Driver’s Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning
- Source :
- Sensors; Volume 21; Issue 23; Pages: 8019, Sensors, Vol 21, Iss 8019, p 8019 (2021), Sensors (Basel, Switzerland)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- 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.
- Subjects :
- eye-movement
Automobile Driving
Eye Movements
Computer science
Feature extraction
Image processing
TP1-1185
Machine learning
computer.software_genre
Biochemistry
Convolutional neural network
Article
Analytical Chemistry
cognitive load
machine learning
non-contact
Cognition
Deep Learning
Humans
Electrical and Electronic Engineering
Instrumentation
business.industry
Deep learning
Chemical technology
Accidents, Traffic
Atomic and Molecular Physics, and Optics
Support vector machine
Alertness
Domain knowledge
Artificial intelligence
business
computer
Cognitive load
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Database :
- OpenAIRE
- Journal :
- Sensors; Volume 21; Issue 23; Pages: 8019
- Accession number :
- edsair.doi.dedup.....5b3a5eefaf5b2ac5419f8ef966979a33
- Full Text :
- https://doi.org/10.3390/s21238019