Driver Fatigue Research Articles

Driver Identification and Detection of Drowsiness while Driving

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 delineated two-phase project is tailored to strengthen security measures and address accidents stemming from driver distraction and fatigue. The initial phase is centered on facial recognition for driver authentication before vehicle initiation. Following successful authentication, the subsequent phase harnesses continuous eye monitoring features, leveraging edge computing for real-time processing to identify signs of drowsiness during the journey. Emphasis is placed on video-based identification and analysis to ensure robust drowsiness detection. Finally, the study highlights the potential of these innovations to revolutionize automotive security and accident prevention within the context of intelligent transport systems.

Enhanced Driver Drowsiness Detection Solution with CNN Algorithm Integration

Enhanced Driver Drowsiness Detection Solution with CNN Algorithm Integration

How to Counteract Driver Fatigue during Conditional Automated Driving—A Systematic Review

This paper summarizes the research on countermeasures against driver fatigue based on a comprehensive systematic literature review. Driver fatigue, induced by task monotony during conditional automated driving (CAD, SAE Level 3), can increase the risk of road accidents. There are several measures that counteract driver fatigue and aim to reduce the risk caused by a fatigued driver in the context of CAD. Twelve selected articles focusing on driver fatigue countermeasures in CAD were analyzed. The findings and conclusions are presented, focusing on the countermeasures themselves and their implementation. The countermeasures were critically discussed, especially regarding effectiveness and applicability. They seem to be effective in counteracting driver fatigue. However, the measures are not easily compared because they were studied in various experimental settings and various driver fatigue measurements were used. Different countermeasures have proven to be effective in reducing fatigue during CAD. For this reason, further investigation is needed to gain further insights into their applications, advantages, and disadvantages. Further studies will be conducted to verify the best solution regarding their effectiveness and applicability.

Stay Alert: Drowsiness Detection with IoT Technology

The "Drowsiness Detection using IoT with Eye Blink Sensor and Buzzer" project aims to address the critical issue of driver fatigue and drowsiness, a major cause of road accidents. Leveraging Internet of Things (IoT) technologies and a combination of eye blink sensors and a buzzer, this system provides a real-time solution for monitoring and alerting drivers when signs of drowsiness are detected. The project focuses on enhancing road safety by preventing accidents caused by driver fatigue Drowsiness detection is a critical aspect of ensuring safety, particularly in domains such as transportation where fatigue-related accidents pose significant risks. In recent years, the proliferation of Internet of Things (IoT) devices has enabled innovative approaches to drowsiness detection systems. This abstract outlines a novel framework for drowsiness detection utilizing IoT technologies. Our proposed system integrates various sensors, including facial recognition cameras, heart rate monitors, and accelerometers, into a networked IoT infrastructure. These sensors continuously monitor key physiological and behavioral indicators associated with drowsiness, such as eye movement patterns, blink frequency, heart rate variability, and head position changes.

Design, Simulation, Building, and Testing of a Microcontroller-Based Automatic Drowsiness Detection, Vehicle Braking, and Alert System

Aims: The premier practical aspect of this research drive is to propose, build, simulate, and evaluate an Arduino-built automatic vehicle driver drowsiness detection and alert system.
 Study Design: The pivotal role of a reliable braking system in vehicular safety cannot be overstated, particularly considering the escalating frequency of traffic accidents, notably prevalent in Indonesia where human factors take center stage as the primary accident catalyst. An insightful poll underscores physical fatigue or drowsiness while driving as the foremost concern. Acknowledging the nuanced disparities between conventional air systems and electric systems, this research strategically directs its attention toward crafting a new system characteristic that mirrors the efficiency of the existing systems.
 Place and Period of Study: The research action engaged by the authors in a bunch of two students under the command of a professor as a part of one of his course capstone projects for the Bachelor of Science in Electrical and Electronic Engineering degree at the American International University Bangladesh (AIUB), Dhaka, Bangladesh. The authors performed their investigative tasks at AIUB from June 2023 to January 2024.
 Methodology: Recognizing the imperative to fortify vehicular safety, the integration of artificial intelligence emerges as a vital solution to assist drivers in ensuring the safety of individuals both within and outside the vehicle. This focused study, tailored specifically for Electric Vehicles (EVs), centers on the innovative application of object and distance identification methodologies to provide a comprehensive braking action indicator. Leveraging a minicomputer and a sophisticated neural network approach, images captured by a stereo camera undergo meticulous machine learning processes. This facilitates the precise categorization and measurement of distances between objects, with subsequent priority decisions determining the optimal degree of braking action. Employing an intelligent methodology, specifically fuzzy logic, the study demonstrates a successful outcome by constructing a curve while concurrently enhancing the dynamics of the pre-existing system. Moreover, a finely tuned Pulse Width Modulation (PWM) signal for braking, lasting 10 milliseconds, is intricately devised based on the study's discerning results.
 Results: The system is simulated in Proteus and tested in real time to check its functionality. The outcomes of the test showed that the system can produce the appropriate signals based on the detection of any kind of driver’s drowsiness and can stop the car.
 Conclusion: This comprehensive approach ensures the seamless replacement of components while concurrently elevating the overall performance of the braking system

IoT-enabled Biometric Security: Enhancing Smart Car Safety with Depth-based Head Pose Estimation

Advanced Driver Assistance Systems (ADAS) are experiencing higher levels of automation, facilitated by the synergy among various sensors integrated within vehicles, thereby forming an Internet of Things (IoT) framework. Among these sensors, cameras have emerged as valuable tools for detecting driver fatigue and distraction. This study introduces HYDE-F, a Head Pose Estimation (HPE) system exclusively utilizing depth cameras. HYDE-F adeptly identifies critical driver head poses associated with risky conditions, thus enhancing the safety of IoT-enabled ADAS. The core of HYDE-F’s innovation lies in its dual-process approach: it employs a fractal encoding technique and keypoint intensity analysis in parallel. These two processes are then fused using an optimization algorithm, enabling HYDE-F to blend the strengths of both methods for enhanced accuracy. Evaluations conducted on a specialized driving dataset, Pandora, demonstrate HYDE-F’s competitive performance compared to existing methods, surpassing current techniques in terms of average Mean Absolute Error (MAE) by nearly 1 ∘ . Moreover, case studies highlight the successful integration of HYDE-F with vehicle sensors. Additionally, HYDE-F exhibits robust generalization capabilities, as evidenced by experiments conducted on standard laboratory-based HPE datasets, i.e., Biwi and ICT-3DHP databases, achieving an average MAE of 4.9 ∘ and 5 ∘ , respectively.

Deep-Learning-Assisted Sensor with Multiple Perception Capabilities for an Intelligent Driver Assistance Monitoring System.

Driver assistance systems can help drivers achieve better control of their vehicles while driving and reduce driver fatigue and errors. However, the current driver assistance devices have a complex structure and severely violate the privacy of drivers, hindering the development of driver assistance technology. To address these limitations, this article proposes an intelligent driver assistance monitoring system (IDAMS), which combines a Kresling origami structure-based triboelectric sensor (KOS-TS) and a convolutional neural network (CNN)-based data analysis. For different driving behaviors, the output signals of the KOS-TSs contain various features, such as a driver's pressing force, pressing time, and sensor triggering sequence. This study develops a multiscale CNN that employs different pooling methods to process KOS-TS data and analyze temporal information. The proposed IDAMS is verified by driver identification experiments, and the results show that the accuracy of the IDAMS in discriminating eight different users is improved from 96.25% to 99.38%. In addition, the results indicate that IDAMS can successfully monitor driving behaviors and can accurately distinguish between different driving behaviors. Finally, the proposed IDAMS has excellent hands-off detection (HOD), identification, and driving behavior monitoring capabilities and shows broad potential for application in the fields of safety warning, personalization, and human-computer interaction.

Survey Paper on the Driver Drowsiness Detection using IoT

Traffic accidents caused by drowsy drivers represent a crucial threat to public safety. Recent statistics show that drowsy drivers cause an estimated 15.5% of fatal accidents. With the widespread use of mobile devices and roadside units, these accidents can be significantly prevented using a drowsiness detection solution. This device would be a valuable tool for helping to keep drivers safe on the road. It would be especially beneficial for drivers who are prone to drowsiness, such as long-haul truck drivers or shit workers. The system works by collecting data from the various sensors and using a machine learning algorithm. While several solutions were proposed in the literature, they all fall short of presenting a distributed architecture that can answer the needs of these applications without breaching the driver’s privacy.

Effects of daylight and darkness at daytime versus nighttime on driver sleepiness: A driving simulator study

The study explores the impact of light conditions on driver sleepiness. In a driving simulator experiment, 20 drivers drove both during daytime in an alert condition and then later at night after being awake since early morning. Light conditions were manipulated by driving in a simulated nighttime scenario in a dark room (1 lx) versus driving in simulated daylight in a room lit with light emitting diodes combining blue light with a yellow phosphor giving a two peaked spectrum (212 lx). Both the daylight and the darkness scenarios were driven daytime and nighttime in a 2 × 2 design. Sleepiness was measured during the four 1-hour drives in terms of subjective sleepiness ratings, divided attention ability, driving performance, heart rate variability and blink behaviour. Significant differences were found in all measured sleepiness indicators between the daytime and nighttime drives, and in most indicators for time-on- task. No significant main effects were found between simulated daylight and darkness. A psychomotor vigilance test conducted before and after each drive also showed no significant effects for lighting condition. Further research, preferably using longitudinal studies in more realistic settings on real roads, is needed to determine which behaviours and which cognitive processes that are affected when driving in daylight versus darkness.

Towards a system for real-time prevention of drowsiness-related accidents

<p>Traffic accidents always result in great human and material losses. One of the main causes of accidents is the human factor, which usually results from driver’s fatigue or drowsiness. To address this issue, several methods for predicting the driver’s state and behavior have been proposed. Some approaches are based on the measurement of the driver’s behavior such as: head movement, blinking time, mouth expression note, while others are based on physiological measurements to obtain information about the internal state of the driver. Several works used machine learning/deep learning to train models for driver behavior prediction. In this paper, we propose a new deep learning architecture based on residual and feature pyramid networks (FPN) for driver drowsiness detection. The trained model is integrated into a system that aims to prevent drowsinessrelated accidents in real-time. The system can detect drivers’ drowsiness in real time and alert the driver in case of danger. Experiment results on benchmarking datasets shows that our proposed architecture achieves high detection accuracy compared to baseline approaches.</p>

Drowsiness and Crash Detection Mobile Application for Vehicle’s Safety

Detecting road accidents promptly is crucial for minimizing casualties and property damage worldwide. The proposed system, comprising hardware and a mobile application, automatically identifies and reports accidents to emergency services. It also employs a facial recognition system to detect driver drowsiness, enhancing accident prevention measures. By leveraging sensor technologies, cellular networks, and advanced detection algorithms, the proposed system analyzes data from accelerometers, Global System for Mobile Communication (GSM), and Global Positioning System (GPS) sensors. Originally designed for vehicles, it can be easily adapted for deployment in various settings such as factories and construction sites with minor adjustments. The system continuously monitors the driver's facial expressions and activities using sensors. When drowsiness is detected, it activates a buzzer, and in the event of a crash, it alerts the driver to prevent false alarms while simultaneously notifying the rescue center if a genuine crash has occurred. This integrated approach enhances safety and optimizes emergency response efforts. The Arduino microcontroller, equipped with an accelerometer, identifies sudden changes in motion like acceleration and rotation to assess impacts against predefined thresholds. Furthermore, GPS functionality accurately determines the vehicle's location at the time of the accident, while GSM enables seamless communication with rescue centers through notifications.

Hearing of warning sounds under the settings of railway running noise considering age-related hearing loss

Railroad drivers may become drowsy because of their irregular work schedules, such as late nights and early mornings. Therefore, in our research, we developed a system that detects driver drowsiness using images based on facial expressions and issues a warning sound to assist drivers during driving. However, it is necessary to function warning sounds even in loud running noise areas or various running noise qualities to make the system more practical. Moreover, it is necessary to consider the effect of a driver’s age-related hearing loss on the setting of the warning sound volume in noisy environments, even for those who meet the hearing criteria as drivers. This is because the number of elderly drivers in their 60 s will increase in the future. Therefore, we investigated the appropriate volume of the warning sound under various running noise types and levels. Then, we compared it between groups of people in their early 20 s and 60 s regarding their hearing ability and the way they hear warning sounds under running noise. The results provide new insight into the relation between hearing ability and the ability to hear warning sounds under driving noise.

Woozy Driving Detection

Abstract: Abstract-wooziness has been identified as one of the primary causes of injury or death in car accidents. As a result, it is proposed in this research work to develop an adaptive heavy vehicle driver fatigue and alertness modelbased on spectral bands by combining signal processing algorithms with soft computing techniques such as the neuro-fuzzy algorithm to estimate the driver cognitive state while driving a vehicle in a virtual reality (vr)-based dynamic simulator under monotonous driving conditions.As a result, it is proposed in this study to use soft computing approaches to reduce the amount of features and nonlinear supervised classification algorithms to classify them. The suggested adaptive model distinguishes between the driver's level of exhaustion by analysing brain responses to determine whether the driver is fatigued owing to task- induced variables or attitude/behaviour, and then the amount of fatigue is associated to sleepiness (i.e. Level of alertness towards driving). The adaptive model can also be used to warn drivers and regulators about impending catastrophes by optimizing the features of interface systems. The suggested system notifies the driver when he or she is fatigued or drowsy based on the identification of cognitive state and calculates the fatigue index and alertness level. The proposed method also assists the driverin being more alert and intuitive in order to avoid deadly road accidents.

Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions.

The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver's alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle's commands.

Research on Driver Drowsiness Detection Method based on Deep Learning

Research on Driver Drowsiness Detection Method based on Deep Learning

Driver body condition monitoring system based on human-computer interaction

Tired driving is still a significant cause of traffic accidents, often causing huge economic losses to society.Therefore, how to avoid driver fatigue has become a meaningful problem. The purpose of this paper is to review and summarize various research methods of driver fatigue monitoring system at home and abroad, so as to reduce the probability of traffic accidents caused by fatigue driving. The main content of this paper is to review the existing fatigue driving monitoring technologies such as electrocardiogram (ECG), electroencephalogram (EEG), HRV, eye tracking system, CNN, facial state analysis, emotion recognition, 5G and brain wave recognition, and infer the fatigue level of drivers by using these technologies to extract various clues that usually represent the level of human vigilance. Assess cognitive load and identify emotional states to monitor tired driving. By discussing the advantages and disadvantages of various fatigue monitoring systems at home and abroad, this paper proposes an innovative method, which transforms the previous single-mode monitoring system into a multi-mode monitoring system, integrates multiple biosensors, extracts relevant features from the biosensor data and processes the data reasonably in order to understand the drivers physiological state more comprehensively. This method is expected to improve the accuracy of fatigue monitoring system.

Fatigue in taxi drivers and its relationship with traffic accident history and experiences: a cross-sectional study in the north of Iran

BackgroundThe monotonous nature of work, long driving duration, and working overload hours cause frequent fatigue in taxi drivers. A high prevalence of fatigue is associated with traffic accidents. However, the risk factors associated with taxi driver fatigue are unclear. Therefore, the present study aims to determine the rate of fatigue in taxi drivers and its relationship to their traffic accident experience.MethodsIn this descriptive-analytical study, 400 taxi drivers in the city of Rasht were registered in Taxi association selected through random sampling and entered into the study based on inclusion criteria. Data was collected through a researcher-made questionnaire reliable and valid by two medical students. The statistical analysis used ordinal data and a Poisson regression model with SPSS software version 21, with a significance level set at 5%.ResultsThe driver fatigue self-reported was directly and significantly related to alcohol consumption (OR = 3.43, 95% CI 1.01–11.62) and had a significant and inverse relationship with smoking (OR = 0.50, 95% CI 0.32–0.76), being married (OR = 0.08, 95% CI 0.01–0.40) and driving experience there was (OR = 0.96, 95% CI 0.94–0.98). Drivers’ sense of quality of life (QOL) was directly and significantly related to smoking (IRR = 1.43, 95% CI 1.28–1.59), education level under diploma (IRR = 2.41, 95% CI 1.43–4.06) diploma (IRR = 2.06, 95% CI 1.21–3.48) and bachelor (IRR = 2.42, 95% CI 1.36–4.29) and there was a significant and inverse relationship with age (IRR = 0.98, 95%CI 0.98–0.99). There was a significant relationship between the number of traffic accidents in the past year with the level of bachelor’s degree (IRR = 3.10, 95% CI 1.43–6.76) and driving experience (IRR = 1.03, 95% CI 1.02–1.04 and inverse relationship between the number of traffic accidents in the past year and the QOL sense (IRR = 0.96, 95% CI 0.93–0.99) and the working hours (IRR = 0.96, 95% CI 0.94–0.99).ConclusionLegislators and policymakers should pay more attention to fatigue in single and inexperienced taxi drivers. Regarding the QOL, pay attention to drivers with high education and older. To reduce the number of crashes, pay more attention to drivers with a bachelor’s degree and less driving experience and improve the feeling of QOL.

A generative adaptive convolutional neural network with attention mechanism for driver fatigue detection with class-imbalanced and insufficient data

Over the past few years, fatigue driving has emerged as one of the main causes of traffic accidents, necessitating the development of driver fatigue detection systems. However, many existing methods involves tedious manual parameter tunings, a process that is both time-consuming and results in task-specific models. On the other hand, most of the researches on fatigue recognition are based on class-balanced and sufficient data, and effectively “mine” meaningful information from class-imbalanced and insufficient data for fatigue recognition is still a challenge. In this paper, we proposed two novel models, the attention-based residual adaptive multiscale fully convolutional network-long short term memory network (ARMFCN-LSTM), and the Generative ARMFCN-LSTM (GARMFCN-LSTM) aiming to address this issue. ARMFCN-LSTM excels at automatically extracting multiscale representations through adaptive multiscale temporal convolutions, while capturing temporal dependency features through LSTM. GARMFCN-LSTM integrates Wasserstein GAN with gradient penalty (WGAN-GP) into ARMFCN-LSTM to improve driver fatigue detection performance by alleviating data scarcity and addressing class imbalances. Experimental results show that ARMFCN-LSTM achieves the highest classification accuracy of 95.84% in driver fatigue detection on the class-balanced EEG dataset (binary classification), and GARMFCN-LSTM attained an improved classification accuracy of 84.70% on the class-imbalanced EOG dataset (triple classification), surpassing the competing methods. Therefore, the proposed models are promising for further implementations in online driver fatigue detection systems.

Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems

Currently, the percentage of traffic accidents has increased, and according to statistics, this percentage will continue to increase every year, so it is necessary to develop new technologies to prevent this kind of accidents. This paper presents a drowsiness detection system based on electroencephalogram (EEG) signals using a pair of channels (Fp1 and Fp2) applied to drivers before entering their vehicles. First, this model detects the relationship between the area under the curve (AUC) of alpha brain waves, an effective parameter for detecting drowsiness. Then, the extracted information is passed to a fuzzy expert system (FES) that classifies the subject's state as "alert" or "sleepy"; the criterion used was a threshold and training with subjective levels. The proposed system was compared with neural network models, such as support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF). Measurements of one hundred and twenty minutes were performed on each of the ten drivers for two days to test the system. The tests confirm that this system is suitable for preventive measures and that the fuzzy system is superior to traditional neural network methods.

Enhancing Road Safety: Deep Learning-Based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems

Driver drowsiness detection is a significant element of Advanced Driver-Assistance Systems (ADASs), which utilize deep learning (DL) methods to improve road safety. A driver drowsiness detection system can trigger timely alerts like auditory or visual warnings, thereby stimulating drivers to take corrective measures and ultimately avoiding possible accidents caused by impaired driving. This study presents a Deep Learning-based Intelligent Driver Drowsiness Detection for Advanced Driver-Assistance Systems (DLID3-ADAS) technique. The DLID3-ADAS technique aims to enhance road safety via the detection of drowsiness among drivers. Using the DLID3-ADAS technique, complex features from images are derived through the use of the ShuffleNet approach. Moreover, the Northern Goshawk Optimization (NGO) algorithm is exploited for the selection of optimum hyperparameters for the ShuffleNet model. Lastly, an extreme learning machine (ELM) model is used to properly detect and classify the drowsiness states of drivers. The extensive set of experiments conducted based on the Yawdd driver database showed that the DLID3-ADAS technique achieves a higher performance compared to existing models, with a maximum accuracy of 97.05% and minimum computational time of 0.60 s.