A review of EEG signal features and their application in driver drowsiness detection systems
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that
is often approached using neurophysiological signals as the basis for building a reliable …
is often approached using neurophysiological signals as the basis for building a reliable …
Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review
There are several factors for vehicle accidents during driving such as drivers' negligence,
drowsiness, and fatigue. These accidents can be avoided, if drivers are warned in time …
drowsiness, and fatigue. These accidents can be avoided, if drivers are warned in time …
Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches
Introduction: Drowsiness is one of the main contributors to road-related crashes and
fatalities worldwide. To address this pressing global issue, researchers are continuing to …
fatalities worldwide. To address this pressing global issue, researchers are continuing to …
Automatic driver sleepiness detection using EEG, EOG and contextual information
The many vehicle crashes that are caused by driver sleepiness each year advocates the
development of automated driver sleepiness detection (ADSD) systems. This study …
development of automated driver sleepiness detection (ADSD) systems. This study …
HCF: A hybrid CNN framework for behavior detection of distracted drivers
C Huang, X Wang, J Cao, S Wang, Y Zhang - IEEE access, 2020 - ieeexplore.ieee.org
Distracted driving causes a large number of traffic accident fatalities and is becoming an
increasingly important issue in recent research on traffic safety. Gesture patterns are less …
increasingly important issue in recent research on traffic safety. Gesture patterns are less …
Continuous EEG decoding of pilots' mental states using multiple feature block-based convolutional neural network
Non-invasive brain-computer interface (BCI) has been developed for recognizing and
classifying human mental states with high performances. Specifically, classifying pilots' …
classifying human mental states with high performances. Specifically, classifying pilots' …
Heart rate variability for classification of alert versus sleep deprived drivers in real road driving conditions
A Persson, H Jonasson, I Fredriksson… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Driver sleepiness is a contributing factor in many road fatalities. A long-standing goal in
driver state research has therefore been to develop a robust sleepiness detection system. It …
driver state research has therefore been to develop a robust sleepiness detection system. It …
Driver fatigue detection systems using multi-sensors, smartphone, and cloud-based computing platforms: a comparative analysis
Q Abbas, A Alsheddy - Sensors, 2020 - mdpi.com
Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities
to decrease traffic accidents caused by driver fatigue while driving on the road …
to decrease traffic accidents caused by driver fatigue while driving on the road …
CSF-GTNet: A novel multi-dimensional feature fusion network based on Convnext-GeLU-BiLSTM for EEG-signals-enabled fatigue driving detection
D Gao, P Li, M Wang, Y Liang, S Liu… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Electroencephalography (EEG) signal has been recognized as an effective fatigue detection
method, which can intuitively reflect the drivers' mental state. However, the research on multi …
method, which can intuitively reflect the drivers' mental state. However, the research on multi …
[PDF][PDF] Deriving Driver Behavioral Pattern Analysis and Performance Using Neural Network Approaches.
It has been observed that driver behavior has a direct and considerable impact upon factors
like fuel consumption, environmentally harmful emissions, and public safety, making it a key …
like fuel consumption, environmentally harmful emissions, and public safety, making it a key …