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 …

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 …

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 …

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 …

[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 …

[كتاب][B] Integration of ai-based manufacturing and industrial engineering systems with the Internet of Things

P Bhambri, S Rani, VE Balas, AA Elngar - 2023‏ - books.google.com
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 …

Lightweight multilayer random forests for monitoring driver emotional status

M Jeong, J Nam, BC Ko - Ieee Access, 2020‏ - ieeexplore.ieee.org
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 …

Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short‐term memory units

Z **ao, Z Hu, L Geng, F Zhang, J Wu… - IET Intelligent Transport …, 2019‏ - Wiley Online Library
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 …

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 …

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 …