The adequate automatic detection of driver fatigue is a very valuable approach for the prevention of traffic accidents. Devices that can determine drowsiness conditions accurately must inherently be portable, adaptable to different vehicles and drive...
Drowsiness and fatigue are major safety issues that cannot be measured directly. Their measurements are sustained on indirect parameters such as the effects on driving performance, changes in physiological states, and subjective measures. We divided...
In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system whic...
Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physica...
29 January 2016
Drowsiness is the intermediate state between wakefulness and sleep. It is characterized by impairments of performance, which can be very dangerous in many activities and can lead to catastrophic accidents in transportation or in industry. There is th...
This paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delin...
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are...
Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have b...
Due to a reduction in reaction time and, consequently, the driver’s concentration, driving when fatigued has become an issue throughout time. Consequently, the likelihood of having an accident and it being fatal increases. In this work, we aim...
The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of...
As a cause of accidents, drowsiness can cause economical and physical damage. A range of drowsiness estimation methods have been proposed in previous studies to aid accident prevention and address this problem. However, none of these methods are able...
Drowsy driving is one of the common causes of road accidents resulting in injuries, even death, and significant economic losses to drivers, road users, families, and society. There have been many studies carried out in an attempt to detect drowsiness...
Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early...
Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric inf...
Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artific...
Numerous reports state that drowsiness is one of the major factors affecting driving performance and resulting in traffic accidents. In the past, methods to detect driver drowsiness have been developed based on physiological, behavioral, and vehicula...
Drowsiness is a major cause of fatal accidents, in particular in transportation. It is therefore crucial to develop automatic, real-time drowsiness characterization systems designed to issue accurate and timely warnings of drowsiness to the driver. I...
Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. Howeve...
Objective detection of a driver’s drowsiness is important for improving driving safety, and the most prominent indicator of drowsiness is changes in electroencephalographic (EEG) activity. Despite extensively documented behavioral differences b...
Drowsiness is one of the leading causes of traffic accidents. For those who operate large machinery or motor vehicles, incidents due to lack of sleep can cause property damage and sometimes lead to grave consequences of injuries and fatality. This st...
Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram...
Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness dete...
Drowsy driving significantly impairs drivers’ attention and reaction times, increasing the risk of accidents. Developing effective prevention technologies is therefore a critical task. Previous studies have highlighted several limitations: (1)...
Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes...
Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness...
Nowadays, we live in the smart mobility era in which vehicles are equipped with small sensing devices to collect various road information. With such sensors, we are able to provide an overview of what is happening on the road and offer an efficient s...
Drowsiness while driving poses a significant risk in terms of road safety, making effective drowsiness detection systems essential for the prevention of accidents. Facial signal-based detection methods have proven to be an effective approach to drows...
Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented...
This study aims to utilize heart rate variability (HRV) signals obtained with a wearable sensor for driver drowsiness detection. To this end, we investigated respiration characteristics derived from HRV signals based on the known fact that respirator...
This work presents the development of an ADAS (advanced driving assistance system) focused on driver drowsiness detection, whose objective is to alert drivers of their drowsy state to avoid road traffic accidents. In a driving environment, it is nece...
Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhanc...
There are a variety of potential uses for the classification of eye conditions, including tiredness detection, psychological condition evaluation, etc. Because of its significance, many studies utilizing typical neural network algorithms have already...
Drowsiness while driving is a common problem for many drivers and a significant problem in contemporary society. This study presents a method for detecting drowsiness while driving. The key finding is that six channels of EEG data are closely associa...
Drowsy driving is one of the main causes of traffic accidents. To reduce such accidents, early detection of drowsy driving is needed. In previous studies, it was shown that driver drowsiness affected driving performance, behavioral indices, and physi...
Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, an approach to detect drowsiness in drivers is presented, focusing on the eye region, since eye fatigue is one of the first sy...
The amount of road accidents caused by driver drowsiness is one of the world’s major challenges. These accidents lead to numerous fatal and non-fatal injuries which impose substantial financial strain on individuals and governments every year....
Driver inattention is a major contributor to road crashes. The emerging of new driver monitoring systems represents an opportunity for researchers to explore new data sources to understand driver inattention, even if the technology was not developed...
Drowsiness-related car accidents continue to have a significant effect on road safety. Many of these accidents can be eliminated by alerting the drivers once they start feeling drowsy. This work presents a non-invasive system for real-time driver dro...
The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI)...
Drowsy driving poses a significant challenge to road safety worldwide, contributing to thousands of accidents and fatalities annually. Despite advancements in driver drowsiness detection (DDD) systems, many existing methods face limitations such as i...
Drowsiness is a risk to human lives in many occupations and activities where full awareness is essential for the safe operation of systems and vehicles, such as driving a car or flying an airplane. Although it is one of the main causes of many road a...
Drowsiness poses a serious challenge to road safety and various in-cabin sensing technologies have been experimented with to monitor driver alertness. Cameras offer a convenient means for contactless sensing, but they may violate user privacy and req...
Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studi...
Driver drowsiness is a major contributor to road accidents, often resulting from delayed reaction times and impaired cognitive performance. This study introduces EffRes-DrowsyNet, a hybrid deep learning model that integrates the architectural efficie...
Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates feature...
Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such met...
This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work prope...
Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection sys...
Driver fatigue reduces the safety of traditional driving and limits the widespread adoption of self-driving cars; hence, the monitoring and early detection of drivers’ drowsiness plays a key role in driving automation. When representing the dro...
Construction projects’ unsatisfactory performance has been linked to factors influencing individuals’ well-being and mental alertness on projects. Drowsiness is a significant indicator of sleep deprivation and fatigue, so being able to id...
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