Results 1 to 10 of about 183 (113)

Driver Distraction Detection Methods: A Literature Review and Framework

IEEE Access, 2021
Driver inattention and distraction are the main causes of road accidents, many of which result in fatalities. To reduce road accidents, the development of information systems to detect driver inattention and distraction is essential.
Alexey Kashevnik, Roman Shchedrin, Christian Kaiser, Alexander Stocker   +3 more
doaj   +3 more sources

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   +2 more sources

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   +3 more sources

Driver Distraction Using Visual-Based Sensors and Algorithms

Sensors, 2016
Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway.
Alberto Fernández, Rubén Usamentiaga, Juan Luis Carús, Rubén Casado   +3 more
doaj   +3 more sources

Driver Distraction Detection in Extreme Conditions Using Kolmogorov–Arnold Networks

Computers
Driver distraction can have severe safety consequences, particularly in public transportation. This paper presents a novel approach for detecting bus driver actions, such as mobile phone usage and interactions with passengers, using Kolmogorov–Arnold ...
János Hollósi, Gábor Kovács, Mykola Sysyn, Dmytro Kurhan, Szabolcs Fischer, Viktor Nagy   +5 more
doaj   +3 more sources

Highly Discriminative Driver Distraction Detection Method Based on Swin Transformer

Vehicles
Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient ...
Ziyang Zhang, Lie Yang, Chen Lv
doaj   +3 more sources

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

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

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Sensors, 2023
Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents.
Efi Papatheocharous, Christian Kaiser, Johanna Moser, Alexander Stocker   +3 more
doaj   +1 more source

Temporal Dashboard Gaze Variance (TDGV) Changes for Measuring Cognitive Distraction While Driving

Sensors, 2022
A difficult challenge for today’s driver monitoring systems is the detection of cognitive distraction. The present research presents the development of a theory-driven approach for cognitive distraction detection during manual driving based on temporal ...
Cyril Marx, Elem Güzel Kalayci, Peter Moertl   +2 more
doaj   +1 more source