Results 21 to 30 of about 2,034 (255)

The box task - a method for assessing in-vehicle system demand

MethodsX, 2021
The use of advanced in-vehicle information systems (IVIS) and other complex devices such as smartphones while driving can lead to driver distraction, which, in turn, increases safety-critical event risk.
Daniel Trommler, Tina Morgenstern, Elisabeth M. Wögerbauer, Frederik Naujoks, Josef F. Krems, Andreas Keinath   +5 more
doaj   +1 more source

Multimodal Polynomial Fusion for Detecting Driver Distraction [PDF]

Interspeech 2018, 2018
INTERSPEECH ...
Yulun Du, Chirag Raman, Alan W. Black, Louis-Philippe Morency, Maxine Eskénazi   +4 more
openaire   +2 more sources

Detection of Driving Distractions and Their Impacts

Journal of Advanced Transportation, 2023
For decades, road crashes have caused many deaths and injuries and generally have had a severe social and economic impact on societies. According to studies, driver distraction has led to an increase in driving-related risks.
Arian Shajari, Houshyar Asadi, Sebastien Glaser, Adetokunbo Arogbonlo, Shady Mohamed, Lars Kooijman, Ahmad Abu Alqumsan, Saeid Nahavandi   +7 more
doaj   +1 more source

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification

Sensors, 2021
Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent ...
Hong Vin Koay, Joon Huang Chuah, Chee-Onn Chow, Yang-Lang Chang, Bhuvendhraa Rudrusamy   +4 more
doaj   +1 more source

Neuromorphic Driver Monitoring Systems: A Computationally Efficient Proof-of-Concept for Driver Distraction Detection

IEEE Open Journal of Vehicular Technology, 2023
Driver Monitoring Systems (DMS) represent a promising approach for enhancing driver safety within vehicular technologies. This research explores the integration of neuromorphic event camera technology into DMS, offering faster and more localized ...
Waseem Shariff, Mehdi Sefidgar Dilmaghani, Paul Kielty, Joe Lemley, Muhammad Ali Farooq, Faisal Khan, Peter Corcoran   +6 more
doaj   +1 more source

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&#
Apurva Misra, Siby Samuel, Shi Cao, Khatereh Shariatmadari   +3 more
doaj   +1 more source

Automatic driver distraction detection using deep convolutional neural networks

Intelligent Systems with Applications, 2022
Recently, the number of road accidents has been increased worldwide due to the distraction of the drivers. This rapid road crush often leads to injuries, loss of properties, even deaths of the people. Therefore, it is essential to monitor and analyze the
Md. Uzzol Hossain, Md. Ataur Rahman, Md. Manowarul Islam, Arnisha Akhter, Md. Ashraf Uddin, Bikash Kumar Paul   +5 more
doaj   +1 more source

MELD3: Integrating Multi-Task Ensemble Learning for Driver Distraction Detection

IEEE Access
Detecting and alerting distracted drivers is crucial to prevent traffic accidents. Although numerous studies have been proposed that use deep learning methods to detect driver distraction, most of these approaches rely on single-perspective images, which
Gokhan Azizoglu, Ahmet Nusret Toprak
doaj   +1 more source

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

IEEE Access
Driver distraction behavior recognition is crucial for improving driving safety. Traditional end-to-end driver distraction detection models are susceptible to factors such as the driving environment, the in-vehicle background, and the driver ...
Xiaofen Shi
doaj   +1 more source

Driver Distraction Detection Based on Fusion Enhancement and Global Saliency Optimization

Mathematics
Driver distraction detection not only effectively prevents traffic accidents but also promotes the development of intelligent transportation systems.
Xueda Huang, Shuangshuang Gu, Yuanyuan Li, Guanqiu Qi, Zhiqin Zhu, Yiyao An   +5 more
doaj   +1 more source