Results 21 to 30 of about 3,344 (257)

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

Glance behaviours when using an in-vehicle smart driving aid : a real-world, on-road driving study [PDF]

, 2014
In-vehicle information systems (IVIS) are commonplace in modern vehicles, from the initial satellite navigation and in-car infotainment systems, to the more recent driving related Smartphone applications.
Fowkes, Mark, Birrell, Stewart A.
core   +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

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

Effect of using an in-vehicle smart driving aid on real-world driver performance [PDF]

, 2014
A smart driving system (providing both safety and fuel-efficient driving advice in real time in the vehicle) was evaluated in real-world on-road driving trials to see if any measurable beneficial changes in driving performance would be observed.
Fowkes, Mark, Birrell, Stewart A., Jennings, P. A. (Paul A.)   +2 more
core   +1 more source

Driver distraction detection via multi‐scale domain adaptation network

IET Intelligent Transport Systems, 2023
Distracted driving is the leading cause of road traffic accidents. It is essential to monitor the driver's status to avoid traffic accidents caused by distracted driving.
Jing Wang, ZhongCheng Wu
doaj   +1 more source

Modeling safety risk perception due to mobile phone distraction among four wheeler drivers

IATSS Research, 2017
Nowadays, there is an increasing trend in the use of information and communication technology devices in new vehicles. Due to these increasing service facilities, driver distraction has become a major concern for transportation safety.
Raghunathan Rajesh, R. Srinath, R. Sasikumar, B. Subin   +3 more
doaj   +1 more source

Which factors lead to driving errors? A structural equation model analysis through a driving simulator experiment

IATSS Research, 2019
As driving error is a main contributory factor of road accidents, its causes and consequences are of great interest in the road safety decision making process.
Panagiotis Papantoniou, George Yannis, Eleni Christofa   +2 more
doaj   +1 more source

Examination of Driver Visual and Cognitive Responses to Billboard Elicited Passive Distraction Using Eye-Fixation Related Potential

Sensors, 2021
Distractions external to a vehicle contribute to visual attention diversion that may cause traffic accidents. As a low-cost and efficient advertising solution, billboards are widely installed on side of the road, especially the motorway.
Yongxiang Wang, William Clifford, Charles Markham, Catherine Deegan   +3 more
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