Results 11 to 20 of about 3,344 (257)

Selection of Measurement Method for Detection of Driver Visual Cognitive Distraction: A Review [PDF]

IEEE Access, 2017
Driving distraction is a topic of great interest in the transport safety-research community, because it is now a primary cause of road accidents. A recent report has revealed that distraction is more alarming than previously thought, and a suitable ...
Norhasliza M. Yusoff, MALIK, Aamir Saeed, Frederic Merienne, CHRISTOPHE, GUILLET, SAAD, Naufal M., YUZOFF, Norhasliza M., Rana Fayyaz Ahmad, MERIENNE, Frédéric, Christophe Guillet, AHMAD, Rana Fayyaz, Naufal M. Saad, Aamir Saeed Malik, GUILLET, Christophe   +12 more
core   +3 more sources

Driver Cognitive Distraction Detection Using Driving Performance Measures [PDF]

Discrete Dynamics in Nature and Society, 2012
Driver cognitive distraction is a hazard state, which can easily lead to traffic accidents. This study focuses on detecting the driver cognitive distraction state based on driving performance measures.
Haijing Hou, Qingning Niu, Dongdong Shi, Yali Wang, Lisheng Jin, Huacai Xian   +5 more
core   +2 more sources

Detection of Driver Cognitive Distraction Using Machine Learning Methods

IEEE Access, 2023
Driver distraction is one of the primary causes of crashes. As a result, there is a great need to continuously observe driver state and provide appropriate interventions to distracted drivers. Cognitive distraction refers to the “look but not see&#
Shi Cao, Apurva Misra, Siby Samuel, Khatereh Shariatmadari   +3 more
core   +2 more sources

Classification of Driver Distraction Risk Levels: Based on Driver’s Gaze and Secondary Driving Tasks

Mathematics, 2022
Driver distraction is one of the significant causes of traffic accidents. To improve the accuracy of accident occurrence prediction under driver distraction and to provide graded warnings, it is necessary to classify the level of driver distraction ...
Lili Zheng, Yanlin Zhang, Tongqiang Ding, Fanyun Meng, Yanlin Li, Shiyu Cao   +5 more
doaj   +1 more source

Deep Learning Approach Based on Residual Neural Network and SVM Classifier for Driver’s Distraction Detection

Applied Sciences, 2022
In the last decade, distraction detection of a driver gained a lot of significance due to increases in the number of accidents. Many solutions, such as feature based, statistical, holistic, etc., have been proposed to solve this problem.
Tahir Abbas, Syed Farooq Ali, Mazin Abed Mohammed, Aadil Zia Khan, Mazhar Javed Awan, Arnab Majumdar, Orawit Thinnukool   +6 more
doaj   +1 more source

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

IEEE Access, 2020
Detecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics.
Adebamigbe Fasanmade, Ying He, Ali H. Al-Bayatti, Jarrad Neil Morden, Suleiman Onimisi Aliyu, Ahmed S. Alfakeeh, Alhuseen Omar Alsayed   +6 more
doaj   +1 more source

Driver Distraction Classification Using Deep Convolutional Autoencoder and Ensemble Learning

IEEE Access, 2023
The study of real-time classification for driver distraction provides new insights into the understanding of behavioral and cognitive reasons behind it. Among various approaches, deep learning models show better performance and can be utilized for a real-
Anirudh Muthuswamy, M. Ali Akber Dewan, Mahbub Murshed, Dharamjit Parmar   +3 more
doaj   +1 more source

Robust Deep Learning-Based Driver Distraction Detection and Classification

IEEE Access, 2021
Driver distraction is a major cause of road accidents. Distracting activities while driving include text messaging and talking on the phone. In this paper, we propose a robust driver distraction detection system that extracts the driver’s state ...
Amal Ezzouhri, Zakaria Charouh, Mounir Ghogho, Zouhair Guennoun   +3 more
doaj   +1 more source

Towards Imbalanced Multiclass Driver Distraction Identification

Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022
Driver distraction is one of the leading causes of driving-related accidents worldwide. The ability to detect driver distraction preemptively is crucial to reducing the number of such accidents.
Kapotaksha Das, Mohamed Abouelenien, Mihai Burzo, Rada Mihalcea   +3 more
doaj   +1 more source

Multimodel System for Driver Distraction Detection and Elimination

IEEE Access, 2022
On average 3,700 people lose their lives on roads every day due to car accidents as a result of drivers’ distraction. In this research, a proposed hybrid approach is presented.
Abdulrahman AbouOuf, Ibrahim Sobh, Mohammad Nasser, Omar Alsaqa, Omar Elezaby, John F. W. Zaki   +5 more
doaj   +1 more source