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A review of recent developments in driver drowsiness detection systems
Y Albadawi, M Takruri, M Awad - Sensors, 2022 - mdpi.com
Continuous advancements in computing technology and artificial intelligence in the past
decade have led to improvements in driver monitoring systems. Numerous experimental …
decade have led to improvements in driver monitoring systems. Numerous experimental …
Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review
SA El-Nabi, W El-Shafai, ESM El-Rabaie… - Multimedia Tools and …, 2024 - Springer
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 …
Real-time machine learning-based driver drowsiness detection using visual features
Y Albadawi, A AlRedhaei, M Takruri - Journal of imaging, 2023 - mdpi.com
Drowsiness-related car accidents continue to have a significant effect on road safety. Many
of these accidents can be eliminated by alerting the drivers once they start feeling drowsy …
of these accidents can be eliminated by alerting the drivers once they start feeling drowsy …
Detection and analysis: Driver state with electrocardiogram (ECG)
S Murugan, J Selvaraj, A Sahayadhas - Physical and engineering sciences …, 2020 - Springer
Driver drowsiness, fatigue and inattentiveness are the major causes of road accidents,
which lead to sudden death, injury, high fatalities and economic losses. Physiological …
which lead to sudden death, injury, high fatalities and economic losses. Physiological …
[HTML][HTML] Survey and synthesis of state of the art in driver monitoring
A Halin, JG Verly, M Van Droogenbroeck - Sensors, 2021 - mdpi.com
Road vehicle accidents are mostly due to human errors, and many such accidents could be
avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing …
avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing …
[كتاب][B] Integration of ai-based manufacturing and industrial engineering systems with the Internet of Things
Integration of AI-Based Manufacturing and Industrial Engineering Systems with the Internet
of Things describes how AI techniques, such as deep learning, cognitive computing, and …
of Things describes how AI techniques, such as deep learning, cognitive computing, and …
Lightweight multilayer random forests for monitoring driver emotional status
This study proposes a lightweight multilayer random forest (LMRF) model, which is a non-
neural network style deep model consisting of layer-by-layer random forests. Although a …
neural network style deep model consisting of layer-by-layer random forests. Although a …
Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short‐term memory units
Fatigue driving has become one of the major causes of traffic accidents. The authors
propose an effective method capable of detecting fatigue state via the spatial–temporal …
propose an effective method capable of detecting fatigue state via the spatial–temporal …
Macroscopic big data analysis and prediction of driving behavior with an adaptive fuzzy recurrent neural network on the internet of vehicles
DC Li, MYC Lin, LD Chou - IEEE Access, 2022 - ieeexplore.ieee.org
Dangerous driving behaviors are diverse and complex. Determining how to analyze the
driving behavior of public drivers objectively and accurately has always been a research …
driving behavior of public drivers objectively and accurately has always been a research …
Hierarchical deep neural networks to detect driver drowsiness
S Jamshidi, R Azmi, M Sharghi, M Soryani - Multimedia Tools and …, 2021 - Springer
Driver drowsiness is one of the main reasons for deadly accidents, especially on suburban
roads. Researchers have used many methods for analyzing videos and detecting …
roads. Researchers have used many methods for analyzing videos and detecting …