Driver Drowsiness Detection Techniques: A Review (original) (raw)
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A Review of Driver Drowsiness Detection Systems: Techniques, Advantages and Limitations
arXiv (Cornell University), 2022
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.
Driver Drowsiness Detection Techniques : A Survey
2017
In today’s world there are many driver assistance systems in existence. These systems include devices like GPS, music system, Bluetooth calling through car speakers, etc. Although these systems add up to the driver’s satisfaction, they also are at times responsible for his / her distraction. These distractions cause major chunk of the road accidents. Also, driver drowsiness / sleepiness is also considered as one of the major reason for fatal road accidents. In this paper we try to address this issue by surveying various techniques / methods to detect the drowsy state of driver. We have compared the features of these methods depending on their cost effectiveness and accuracy.
A Brief Review on Different Driver's Drowsiness Detection Techniques
International Journal of Image, Graphics and Signal Processing(IJIGSP), 2020
Driver drowsiness is the momentous factor in a huge number of vehicle accidents. This driver drowsiness detection system has been valued highly and applied in various fields recently such as driver visual attention monitoring and driver activity tracking. Drowsiness can be detected through the driver face monitoring system. Nowadays smartphone-based application has developed rapidly and thus also used for driver safety monitoring system. In this paper, a detailed review of driver drowsiness detection techniques implemented in the smartphone has been reviewed. The review has also been focused on insight into recent and state-of-the-art techniques. The advantages and limitations of each have been summarized. A comparative study of recently implemented smartphone-based approaches and mostly used desktop-based approaches has also been discussed in this review paper. And the most important thing is this paper helps others to decide better techniques for the effective drowsiness detection. Index Terms-Drowsiness, smartphone-based, desktop-based, driver drowsiness detection, face tracking and feature extraction.
A Systematic Review of Driver Drowsiness Detection using Various Approaches
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.
Detecting driver drowsiness based on sensors: a review
Sensors (Basel, Switzerland), 2012
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) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.
Sensors
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. As a result, it is critical to prevent catastrophic accidents and reduce the financial burden on society caused by driver drowsiness. The research community has primarily focused on two approaches to identify driver drowsiness during the last decade: intrusive and non-intrusive. The intrusive approach includes physiological measures, and the non-intrusive approach includes vehicle-based and behavioral measures. In an intrusive approach, sensors are used to detect driver drowsiness by placing them on the driver’s body, whereas in a non-intrusive approach, a camera is used for drowsiness detection by identifying yawning patterns, eyelid movement and head inclination. Noticeably, most research has been conducted in driver drowsiness detection met...
A Review on Driver Drowsiness Detection Techniques
Number of accidents during driving is increasing day by day and drowsy driving has been implicated as a causal factor in many accidents. Goal of driver drowsiness detection systems is to reduce these accidents. It has been seen that most of the accidents occur due to driver’s fatigue and a small due to inattention factor, therefore this paper reviews driver’s fatigue monitoring techniques in detail with a little overview of others also.
Driver Drowsiness Detection Methods: A Comprehensive survey
International Journal of Research in Advent Technology, 2019
Drowsy driving is one of the main causes for accidents on roads which leads to death. So, detection of fatigue of the driver and indicating it is an active research area. Most of the traditional methods followed either physiological, vehicle or behavioral based methods for drowsiness detection techniques. It is observed that some methods require sensors which are expensive while others are intrusive to the driver which distract the driving. Therefore, a real time driver's drowsiness detection system with low cost and high accuracy is an essential need. This paper presents different traditional methods used in drowsiness detection for over a decade. This study analyses different machine learning methods in drowsiness detection. It also reviews related studies in the period between 2008 and 2018 focusing on different methods used including latest machine learning techniques.
Driver Drowsiness Detection System and Techniques: A Review
Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough according to the experts. Studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. Attention assist can warn of inattentiveness and drowsiness in an extended speed range and notify drivers of their current state of fatigue and the driving time since the last break, offers adjustable sensitivity and, if a warning is emitted, indicates nearby service areas in the COMAND navigation system.
A Survey on Driver’s Drowsiness Detection Techniques
Nowadays, there are many systems are available in market like navigation systems, warning alarm systems etc. to make driver's work easy. Traffic accidents due to human errors cause many deaths and injuries around the world. Drowsiness and sleeping while driving are now identified as one of the reasons behind fatal crashes and highway accidents caused by drivers. Various drowsiness detection techniques researched are discussed in this paper. These techniques are classified and then compared using their features. Computer vision based image processing techniques is one of them. This uses various images of driver to detect drowsiness states using his/her eyes states and facial expressions. This technique is on the focus of this survey paper.