Loss of control over the steering wheel movements
The various measures of driver drowsiness reviewed in this work are based purely on the level of drowsiness induced in the subject, which, in turn, depends on the time of day, duration of the task and the time that has elapsed since the last sleep. However, when developing a better drowsiness detection system, several other issues need to be addressed; the two most important ones are discussed below.
It is not advisable to force a drowsy driver to drive on roads. Consequently, many experiments have been conducted in simulated environments and the results of the experiments are then elaborately studied. Dinges et al. presented various challenges involved in real time drowsiness detection [ 46 ]. The subjective self-assessment of drowsiness can only be obtained from subjects in simulated environments. In real conditions, it is unfeasible to obtain this information without significantly distracting the driver from their primary task. Some researchers have conducted experiments to confirm the validity of simulated driving environments. For example, Blana et al. observed that the mean lateral displacement of the vehicle from the center of the roadway, obtained in real and simulated environments is statistically different for speeds higher than 70 km/h. This finding implies that real-road drivers feel less safe at higher speeds and, as a result, increase their lateral distance. The drivers in a simulated environment, however, did not appear to perceive this risk [ 72 ]. Most experiments using behavioral measures are conducted in a simulated environment and the results indicate that it is a reliable method to detect drowsiness. However, in real driving conditions, the results might be significantly different because a moving vehicle can present challenges such as variations in lighting, change in background and vibration noise, not to mention the use of sunglasses, caps, etc. Philip et al. compared drowsiness in simulated and real conditions and concluded that it can be equally studied in both environments but the reaction time and the sleepiness self-evaluation are more affected in a simulated environment which provides a more monotonous task [ 15 ]. Engstorm et al. observed that the physiological workload and steering activity was higher in a real environment. This result can be interpreted as an indication of increased effort, which seems reasonable given the higher actual risk in real traffic [ 73 ]. Hence, while developing a drowsiness detection system, the simulated environment should be as close to a replica of the real environment as possible.
Each method used for detecting drowsiness has its own advantages and limitations. Vehicle-based measures are useful in measuring drowsiness when a lack of vigilance affects vehicle control or deviation. However, in some cases, there was no impact on vehicle-based parameters when the driver was drowsy [ 26 ], which makes a vehicle-based drowsiness detection system unreliable. Behavioral measures are an efficient way to detect drowsiness and some real-time products have been developed [ 74 ]. However, when evaluating the available real-time detection systems, Lawrence et al. observed that different illumination conditions affect the reliability and accuracy of the measurements [ 74 ]. Physiological measures are reliable and accurate because they provide the true internal state of the driver; however, their intrusive nature has to be resolved. Among all physiological parameters investigated, ECG can be measured in a less intrusive manner. EEG signals require a number of electrodes to be placed on the scalp and the electrodes used for measuring EoG signals are placed near the eye which can hinder driving. Non-obtrusive physiological sensors to estimate the drowsiness of drivers are expected to become feasible in the near future [ 70 , 75 ]. The advantages of physiological measures and the increasing availability of non-intrusive measurement equipment make it beneficial to combine physiological signals with behavioral and vehicle-based measures. A sample drowsiness detection system developed by combining ECG signals, standard deviation of lane position and facial images is shown in Figure 2 .
A sample hybrid drowsiness detection system using multiple sensors.
Few research studies are attempting to detect driver drowsiness by the fusion of different methods [ 14 , 76 – 78 ]. Cheng et al. combined behavioral measures and vehicle based measures and concluded that the reliability and accuracy of the hybrid method was significantly higher than those using single sensors [ 78 ]. Guosheng et al. used a mixture of subjective, behavioral (PERCLOS) and physiological measures (ECG, EEG) to detect drowsiness and found that this combination resulted in a significantly higher success rate than any individual metric. The average square error while removing physiological features were 1.2629, while the average square error for fusion was 0.5269 [ 14 ].
Although hybrid systems using different sensors have not been tested in a real environment, it would be interesting to investigate the ability to detect drowsiness using a combination of physiological signals with other measurements.
In this paper, we have reviewed the various methods available to determine the drowsiness state of a driver. Although there is no universally accepted definition for drowsiness, the various definitions and the reasons behind them were discussed. This paper also discusses the various ways in which drowsiness can be manipulated in a simulated environment. The various measures used to detect drowsiness include subjective, vehicle-based, physiological and behavioral measures; these were also discussed in detail and the advantages and disadvantages of each measure were described. Although the accuracy rate of using physiological measures to detect drowsiness is high, these are highly intrusive. However, this intrusive nature can be resolved by using contactless electrode placement. Hence, it would be worth fusing physiological measures, such as ECG, with behavioral and vehicle-based measures in the development of an efficient drowsiness detection system. In addition, it is important to consider the driving environment to obtain optimal results.
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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 artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.
Keywords: biological-based measures; driver drowsiness detection; hybrid-based measures; image-based measures; vehicle-based measures.
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Vision-based driver monitoring, a non-invasive method designed to identify potentially dangerous operations, has attracted increasing attention in recent years. In this study, a head pitch angle detection method was established to evaluate the driver’s drowsiness. Rather than employing the front facial landmarks to estimate head pitch angle, the proposed method measure this angel directly from driver’s profile face. To meet the requirement of real-time detection, the method applies the YOLOv8 network of single-stage detection and utilizes MobileNetV3 and FasterNet for lightweight improvement. The detector is trained with re-labeled CFP datasets, and real-time speed tests have been performed. Results demonstrate that the non-improved detector can achieve an mAP50 of 97.3% of the keypoints in a single frame, meanwhile realizing the frame rate of 30.41 FPS. After improvement, parameters of the model have been reduced by 21.3% and 40.9% respectively, while the frame rate can be increased to 37.13 FPS and 52.70 FPS, and the mAP50 of keypoints is increased by 0.41% and 0.51%. The results during the in-car experiment have proved that the developed detection method can effectively evaluate the head pitch angle, thus detect the driver’s drowsiness. We provide open-access to the annotated data and pre-trained models in this study.
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The annotated data involved in this study are publicly available via Harvard dataverse. https://doi.org/10.7910/DVN/C6BPDM . Pre-trained detector models and a configuration file for training are also provided in an external file as examples. https://github.com/MengZ-tech/LIGHTWEIGHT-YOLOV8-NETWORK-FOR-DRIVER-PROFILE-FACE-DROWSINESS-DETECTION
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Zhongda Hospital, Southeast University, Nanjing, 210000, China
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Zhang, M., Zhang, F. Lightweight YOLOv8 Networks for Driver Profile Face Drowsiness Detection. Int.J Automot. Technol. (2024). https://doi.org/10.1007/s12239-024-00103-w
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Computer Engineering and Applications Journal
One of the most common causes of traffic accidents is human error. One such factor involves the drowsy drivers that do not focus on the road before them. Driver drowsiness often occurs due to fatigue in long distances or long durations of driving. The signs of a drowsy driver may be detected based on one out of three types of tests; i.e., performance test, physiological test, and behavioural test. Since the physiological and performance tests are quite difficult and expensive to implement, the behavioural test is a good choice to use for detecting early drowsiness. Behaviour-based driver drowsiness detection has been one of the hot research topics in recent years and is still increasingly developing. There are many approaches for behavioural driver drowsiness detection, such as Neural Networks, Multi Layer Perceptron, Support Vector Machine, Vander Lugt Correlator, Haar Cascade, and Eye Aspect Ratio. Therefore, this study aims to conduct a systematic literature review to elaborate o...
Grenze International Journal of Engineering and Technology GIJET
Drowsiness is one of the leading causes of road accidents, hence a monitoring system is required to identify drowsiness. Driver monitoring systems typically detect three sorts of data: biometric, vehicle, and driver graphic. Nowadays, several devices including navigation systems and warning alarm systems are available to help drivers. The human mistake causes numerous traffic fatalities and injuries worldwide. Drowsiness and mapping while driving is widely recognized as contributing factors to deadly car accidents. This article reviews several sleepiness detecting methods. The characteristics of these approaches are categorized and contrasted. One of them is computer vision-based picture processing. It utilizes the driver's eyes and facial gestures to identify tiredness. This survey study focuses on this strategy.
Accident; analysis and prevention
Christophe Bourdin
Not just detecting but also predicting impairment of a car driver's operational state is a challenge. This study aims to determine whether the standard sources of information used to detect drowsiness can also be used to predict when a given drowsiness level will be reached. Moreover, we explore whether adding data such as driving time and participant information improves the accuracy of detection and prediction of drowsiness. Twenty-one participants drove a car simulator for 110min under conditions optimized to induce drowsiness. We measured physiological and behavioral indicators such as heart rate and variability, respiration rate, head and eyelid movements (blink duration, frequency and PERCLOS) and recorded driving behavior such as time-to-lane-crossing, speed, steering wheel angle, position on the lane. Different combinations of this information were tested against the real state of the driver, namely the ground truth, as defined from video recordings via the Trained Obser...
International Journal of Research in Advent Technology
Desanamukula Venkata Subbaiah
2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)
Mkhuseli Ngxande
Advances in Mathematics: Scientific Journal
Muskan Kumar
arXiv (Cornell University)
Kamal Kassmi
Journal of Pharmaceutical Negative Results ¦ Volume 13 ¦ Special Issue 10 ¦ 2022
Jagbeer Singh
The majority of human deaths and injuries are caused by traffic accidents. A million people worldwide die each year due to traffic accident injuries, consistent with the World Health Organization. Drivers who do not receive enough sleep, rest, or who feel weary may fall asleep behind the wheel, endangering both themselves and other road users. The research on road accidents specified that major road accidents occur due to drowsiness while driving. These days, it is observed that tired driving is the main reason to occur drowsiness. Now, drowsiness becomes the main principle for to increase in the number of road accidents. This becomes a major issue in a world which is very important to resolve as soon as possible. The predominant goal of all devices is to improve the performance to detect drowsiness in real time. Many devices were developed to detect drowsiness, which depend on different artificial intelligence algorithms. So, our research is also related to driver drowsiness detection which can identify the drowsiness of a driver by identifying the face and then followed by eye tracking. The extracted eye image is matched with the dataset by the system. With the help of the dataset, the system detected that if eyes were close for a certain range, it could ring an alarm to alert the driver and if the eyes were open after the alert, then it could continue tracking. If the eyes were open then the score that we set decreased and if the eyes were closed then the score increased. This paper focus to resolve the problem of drowsiness detection with an accuracy of 80% and helps to reduce road accidents.
Inattentiveness in drivers is the major contributing factor in road crashes. Inattention can be caused by several reasons and one amongst them is fatigue. Fatigue is the subjective feeling of tiredness which is distinct from weakness. Fatigue can be defined as the state of impairment that can include physical, mental or both the elements associated with lower alertness and reduced performance. Thus performing a physical activity becomes difficult with the increasing fatigue level. Fatigue can have physical or mental causes. Alertness of a person is typically characterized by the various visual cues like eyelid movement, gaze movement, head movement and facial expressions. They can also be deduced from the driver's behaviour with the vehicle like distance maintained between vehicles, lane deviation, steering wheel control, breaking and gearing of the vehicle. Mental state of the driver can best be determined from the Electroencephalogram signals. This paper gives a brief review o...
International Journal of Advances in Engineering Architecture Science and Technology (IJAEAST)
Background: The purpose of this review paper is to develop a driver drowsiness detection system along with an alert system using deep learning techniques. The goal will be to develop a system that can accurately determine whether the driver is sleepy or not. This project uses CNN-based detection of drivers' drowsiness. Objectives: The objectives of this project are to identify the driver's drowsiness, reduce the number of accidents caused by the driver's drowsiness and provide safety to the driver early and cost-effectively. Methods: An integrated approach depends on the eye and mouth closure status (PERCLOS) along with the calculation of the new proposed vector FAR (Facial Aspect Ratio), similarly to EAR and MAR. This helps to find the status of the closed eyes or opened mouth, like yawning, and any frame that has hand gestures like nodding or covering the opened mouth with a hand, as is the innate nature of humans when trying to control sleepiness. Statistical Analysis: The CNN Yolo model algorithm is used to achieve precise results and ensure safety by preventing accidents resulting from driver drowsiness. The system includes a drowsiness detection mechanism that promptly alerts the driver to mitigate the risk of road accidents caused by drowsiness, thereby preventing potential incidents. Findings: Existing systems that rely on specific facial features, such as the ear, nose, and mouth, for drowsiness detection have limitations in accuracy. Applications: We are developing an improved system that considers all facial features for more reliable predictions. Additionally, we have integrated an alarm to alert drivers if they are becoming too drowsy. Improvements: An automatic and efficient drowsiness detection and driver mood predictionbased system is required to be implemented for real-time applications. This will help to reduce road accidents and increase people's safety.
International Journal for Research in Applied Science & Engineering Technology (IJRASET)
IJRASET Publication
We proposed to use this system to minimise the frequency of accidents caused by driver exhaustion, hence improving road safety. This device uses optical information and artificial intelligence to identify driver sleepiness automatically. We use Softmax to find, monitor, and analyse the driver's face and eyes in order to calculate PERCLOS (% of eye closure). It will also employ alcohol pulse detection to determine whether or not the person is normal. Due to extended driving durations and boredom in crowded settings, driver weariness is one of the leading causes of traffic accidents, particularly for drivers of big vehicles (such as buses and heavy trucks).
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IRJET Journal
Rajesh Natarajan
International Journal for Research in Applied Science and Engineering Technology IJRASET
IJERA Journal
International Journal of Image, Graphics and Signal Processing(IJIGSP)
anis-ul-islam rafid , Amit Niloy , Atiqul Islam Chowdhury
Sensors (Basel, Switzerland)
M Murugappan
Aytul Ercil
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT
International Journal IJRITCC
International Journal of All Research Education & Scientific Methods (IJARESM)
Masum Hossain
Priyanka Sharma
Husam Al-Ameen
Implementation of driver's drowsiness assistance model based on eye movements detection, drowsiness detection systems: comparison and technical criteria for industrial deployment, physiological-based driver monitoring systems: a scoping review, survey and synthesis of state of the art in driver monitoring, real-time monitoring of driver distraction: state-of-the-art and future insights., toward safer vehicular transit: implementing deep learning on single channel eeg systems for microsleep detection, "iot-based vehicle monitoring and driver assistance system framework for safety and smart fleet management", mmassist : passive monitoring of driver's attentiveness using mmwave sensors, 44 references, detection and analysis: driver state with electrocardiogram (ecg), a hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability, the monitoring method of driver's fatigue based on neural network, driver drowsiness detection: a comparison between intrusive and non-intrusive signal acquisition methods.
Iot based real-time drowsy driving detection system for the prevention of road accidents, driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm, eeg-based drowsiness estimation for safety driving using independent component analysis, automatic fatigue detection of drivers through pupil detection and yawning analysis, real-time nonintrusive detection of driver drowsiness, related papers.
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E-dnet: an end-to-end dual-branch network for driver steering intention detection.
Fu, Y.; Xue, H.; Fu, J.; Xu, Z. E-DNet: An End-to-End Dual-Branch Network for Driver Steering Intention Detection. Electronics 2024 , 13 , 2477. https://doi.org/10.3390/electronics13132477
Fu Y, Xue H, Fu J, Xu Z. E-DNet: An End-to-End Dual-Branch Network for Driver Steering Intention Detection. Electronics . 2024; 13(13):2477. https://doi.org/10.3390/electronics13132477
Fu, Youjia, Huixia Xue, Junsong Fu, and Zihao Xu. 2024. "E-DNet: An End-to-End Dual-Branch Network for Driver Steering Intention Detection" Electronics 13, no. 13: 2477. https://doi.org/10.3390/electronics13132477
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IMAGES
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COMMENTS
In order to detect the different stages of drowsiness, researchers have studied driver responses and vehicle driving patterns. In this section, we provide a review of the four widely used measures for DDD. The diagram in Figure 1 illustrates all the currently used measures for classifying driver drowsiness levels.
Abstract. Driver drowsiness has emerged as one of the key factors in recent times' traffic accidents, which can result in fatalities, serious physical losses, large monetary losses, and significant property damage. Drowsiness in a driver can be brought on by long hours behind the wheel, drowsiness, fatigue, medicine, difficulty sleeping, and ...
Drowsiness among drivers is a significant contributing factor to traffic injuries, leading to a yearly toll of severe injuries and fatalities. This study reviews the literature in-depth on driving force sleepiness detection systems, considering physiological signals, facial expressions, and riding habits. It not only describes the current approaches for each class but also offers a comparative ...
This paper introduces a literature review of driver drowsiness detection systems based on an analysis of physiological signals, facial features, and driving patterns.
Traffic accidents always cause great material and human losses. One of the most important causes of these accidents is the human factor, which is usually caused by fatigue or drowsiness. To address this problem, several approaches were proposed to predict the driver state. Some solutions are based on the measurement of the driver behavior such as: the head movement, the duration of the blink ...
study determined that 4.8% of trips involved a driver in KSS levels 6-9, for which drowsy driving is present (See Figure 1). Most drowsy drivers report only low levels of drowsiness (Some signs of drowsiness = 3.3%). A driver makes only 0.5% of trips with "Sleepy, some effort to keep alert" and 0.1% with "Extremely Sleepy, fighting sleep".
1. Introduction. According to available statistical data, over 1.3 million people die each year on the road and 20 to 50 million people suffer non-fatal injuries due to road accidents [].Based on police reports, the US National Highway Traffic Safety Administration (NHTSA) conservatively estimated that a total of 100,000 vehicle crashes each year are the direct result of driver drowsiness.
Caryn FH, Rahadianti L (2021) Driver drowsiness detection based on drivers' physical behaviours: a systematic literature review. Comput Eng Appl J 10:161-175. Google Scholar Chang T-C, Wu M-H, Kim P-Z, Yu M-H (2021) Smart driver drowsiness detection model based on analytic hierarchy process. Sens Mater 33:485-497
Literature Review on Driver's Drowsiness and Fatigue Detection. June 2020. DOI: 10.1109/ISCV49265.2020.9204306. Conference: 4th International Conference on Intelligent Systems and Computer ...
Kee ping in view the rising trend in the use of. physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver. drowsiness using ...
Driver Drowsiness is one of the most factors of road accidents, leading to severe injuries and deaths every year. Drowsiness means difficulty staying awake, which can lead to falling asleep. This paper introduces a literature review of driver drowsiness detection systems based on an analysis of physiological signals, facial features, and driving patterns. The paper also presents and details ...
The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets.
Abstract: This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features that can be extracted from the face to infer the level of drowsiness.
Vision-based driver monitoring, a non-invasive method designed to identify potentially dangerous operations, has attracted increasing attention in recent years. In this study, a head pitch angle detection method was established to evaluate the driver's drowsiness. Rather than employing the front facial landmarks to estimate head pitch angle, the proposed method measure this angel directly ...
cussed; however, other issues including drowsiness labelling, early detection and subject diversity are not present in the review. Kaplan et al. [11] highlights ways to detect drowsiness, commercial products available and simulation data collection. Kaplan et al. also expands the review to driver distraction; another cause of deaths on our roads.
Semantic Scholar extracted view of "Lightweight YOLOv8 Networks for Driver Profile Face Drowsiness Detection" by Meng Zhang et al. ... This study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals and discusses the advantages and disadvantages of existing studies and ...
Driver Drowsiness Detection Based on Drivers' Physical Behaviours: A Systematic Literature Review October 2021 Computer Engineering and Applications Journal 10(3):161-175
Therefore, this study aims to conduct a systematic literature review to elaborate on the development and research trends regarding driver drowsiness detection. ... 2018. [34] A. Mittal, K. Kumar, S. Dhamija, and M. Kaur, "Head movement-based driver drowsiness detection: A review of state-of-art techniques," in 2016 IEEE International ...
A literature review on the recent related works in this field of measurement approach based on the measurements of the physiological signals to get information about the internal state of the driver's body is presented. Traffic accidents always cause great material and human losses. One of the most important causes of these accidents is the human factor, which is usually caused by fatigue or ...
review of recent works in the context of drowsiness detection with deep learning is proposed in [37]. This work presents the five steps of the detection system, composed by the video capture, face detection, characteristic extraction, characteristic analysis, and classification. It also discusses the pros and cons of three approaches of ...
The results showed that the RF classifier gave the best. results, with 82.4% accuracy of alert vs slightly drowsy case. In contrast, majority voting. performed the best for alert vs moderately ...
The percentage of eyelid closure over the pupil over time (PERCLOS) is one of the major methods for the detection of the driver's drowsiness. Physiological measurements like electroencephalogram (EEG), electrocardiogram (ECG) [4], capturing eye closure, facial features [5] [6], or driving performance (such as steering characteristics, lane ...
This paper introduces a comprehensive framework for the detection of behaviors indicative of reduced concentration levels among motor vehicle operators, leveraging multimodal image data. By integrating dedicated deep learning models, our approach systematically analyzes RGB images, depth maps, and thermal imagery to identify driver drowsiness and distraction signs. Our novel contribution ...
An advanced driving assistant system (ADAS) is critical for improving traffic efficiency and ensuring driving safety. By anticipating the driver's steering intentions in advance, the system can alert the driver in time to avoid a vehicle collision. This paper proposes a novel end-to-end dual-branch network (EDNet) that utilizes both in-cabin and out-of-cabin data. In this study, we designed ...
DRIVER DROWSINESS DETECTION SYSTEM. School of Computer Engineering, KIIT, BBSR [4] ABSTRACT. This document is a review report on the research conducted and the project made. in the field of ...
Abstract and Figures. The paper analyses the method used to detect driver's drowsiness and proposes the results & solutions on the limited implementation of the various techniques that are used ...