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Title: a review of driver drowsiness detection systems: techniques, advantages and limitations.
Abstract: 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 recently proposed techniques for each class. We have also provided a comparative study of recently published works regarding the accuracy, reliability, hardware requirement, and intrusiveness. We have summarized and discussed the advantages and limitations of each class. As a result, each class of techniques has advantages and limitations. A hybrid system that combines two and more techniques will be efficient, robust, accurate, and used in real-time to take advantage of each technique.
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Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques
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Driver Drowsiness Detection for Road Safety Using Deep Learning
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- First Online: 26 May 2023
- Cite this conference paper
- Parul Saini 40 ,
- Krishan Kumar 40 ,
- Shamal Kashid 40 ,
- Alok Negi 40 &
- Ashray Saini 40
Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1009))
243 Accesses
1 Citations
Detecting sleepiness in drivers while driving is essential in order to prevent accidents and reduce the number of mortality caused by drivers sleeping behind the wheels. With the rapid growth of the population, the number of accidents on the roads has increased dramatically, and many people have died as a result of those accidents. It’s a serious issue that needs to be addressed in order to reduce accidents. According to research conducted by the National Sleep Foundation, roughly 20% of drivers experience drowsiness while driving. These figures paint a terrible picture. This paper discusses a method for reducing accidents due to driver drowsiness. The VGG-16 architecture is used for detecting driver drowsiness and achieved 96.79% accuracy with 0.08 loss.
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Computer Science and Engineering, National Institute of Technology, Srinagar, Uttarakhand, India
Parul Saini, Krishan Kumar, Shamal Kashid, Alok Negi & Ashray Saini
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National Institute of Technology Uttarakhand, Srinagar, India
Hariharan Muthusamy
Eötvös Loránd University, Budapest, Hungary
János Botzheim
Queensland University of Technology, Brisbane, QLD, Australia
Richi Nayak
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Saini, P., Kumar, K., Kashid, S., Negi, A., Saini, A. (2023). Driver Drowsiness Detection for Road Safety Using Deep Learning. In: Muthusamy, H., Botzheim, J., Nayak, R. (eds) Robotics, Control and Computer Vision. Lecture Notes in Electrical Engineering, vol 1009. Springer, Singapore. https://doi.org/10.1007/978-981-99-0236-1_15
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DOI : https://doi.org/10.1007/978-981-99-0236-1_15
Published : 26 May 2023
Publisher Name : Springer, Singapore
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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 ...
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 ...
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 ...
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
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".
This paper introduces a literature review of driver drowsiness detection systems based on an analysis of physiological signals, facial features, and driving patterns.
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 ...
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 ...
Section 2 presents the literature review on drowsiness detection with deep learning networks. ... In order to overcome this issue, we construct a driver drowsiness detection system using deep learning combined with IoT to be able to detect, alert and potentially save a person's life. For our system, a surveillance camera is used to capture ...
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 ...
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. These include eye blinks, head movements and yawning. However, the development of ...
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 studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more ...
Abstract. This work presented analysis and implementation of driver drowsiness, distraction, and detection systems using image processing techniques. The literature review based on drowsiness, distraction, and detection have been taken with their parameters in tabulation form. Flow charts of software and hardware have been presented for the ...
This section describes Drowsiness Detection models [7, 8] and their limitations, along with some deep learning [] processes that can automatically be fea tured directly from the raw data.Babaeian et al. [] introduced a unique technique for measuring driver drowsiness that uses biomedical signal analysis based on machine learning and is applied to heart rate variation (HRV), which is measured ...
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 which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1 ...
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 ...
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 ...
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 ...
1. This paper relates the street mishaps that happen because of driver's drowsiness. Recent studies stat e that more disasters. are caused due to doziness. Drivers can feel dr. owsiness due to sle ...