Results 11 to 20 of about 5,150 (254)

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

CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection

IEEE Access, 2022
Driver distraction behavior is prone to induce traffic accidents. Therefore, it is necessary to detect it to caution drivers in time for traffic safety. In driver behavior recognition, the diversity of behaviors and driving environment can have a certain
Shugang Liu, Yujie Wang, Qiangguo Yu, Hongli Liu, Zhan Peng   +4 more
doaj   +1 more source

Distraction Potential of Vehicle-Based On-Road Projection

Applied Sciences, 2021
With regard to autonomous driving, on-road projections cannot only be used for communication with the driver but also with other road users. Our study aims to investigate the distraction potential for other road users when on-road projections (e.g., for ...
Tobias Glück, Tobias Biermann, Alexander Wolf, Sören Budig, Arved Ziebehl, Marvin Knöchelmann, Roland Lachmayer   +6 more
doaj   +1 more source

Driver Cognitive Distraction Detection Using Driving Performance Measures

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.
Lisheng Jin, Qingning Niu, Haijing Hou, Huacai Xian, Yali Wang, Dongdong Shi   +5 more
doaj   +1 more source

A Cascaded Multimodal Natural User Interface to Reduce Driver Distraction

IEEE Access, 2020
Natural user interfaces (NUI) have been used to reduce driver distraction while using in-vehicle infotainment systems (IVIS), and multimodal interfaces have been applied to compensate for the shortcomings of a single modality in NUIs.
Myeongseop Kim, Eunjin Seong, Younkyung Jwa, Jieun Lee, Seungjun Kim   +4 more
doaj   +1 more source