Driver Sleepiness Detection System Based on Eye Movements Variables

Jin, Lisheng; Niu, Qingning; Jiang, Yuying; Xian, Huacai; Qin, Yanguang; Xu, Meijiao

doi:10.1155/2013/648431

Cited by 32 publications

(17 citation statements)

References 31 publications

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“…Ahlstrom et al (2013) used AttenD to study the driver distraction, on a database of seven drivers. Smart eyes has also been used by Jin et al (2013), in a simulator to extract blink frequency, gaze direction, fix time and the PERCLOS, then these features are used by Support Vector Machine (SVM) to detect sleepiness among twelve drivers. Miyaji and Kawanaka (2010) have used Adaboost and SVM to classify bio-signals and facial expression data coming from ECG and the eye tracking system FaceLAB.…”

Section: Related Workmentioning

confidence: 99%

“…In short, a large amount of research works have studied the human behavior while driving, using different tools to capture and extract the data (eye-tracking systems, camera with videos processing and machine learning tools, etc. ); these data were processed to extract various features such as PERCLOS (Bergasa and Nuevo 2006;Ji et al 2004;Jin et al 2013;Kong et al 2015;Friedrichs and Yang 2010;Darshana et al 2015;Sun et al 2017) and blink frequency (Bergasa and Nuevo 2006;Jin et al 2013;Friedrichs and Yang 2010;Sun et al 2017Sun et al , 2015. (More details are giving in "Driver's behavior features extraction" section).…”

Section: Related Workmentioning

confidence: 99%

“…In intelligent transportation systems (ITS) field, several research projects have focused on the detection of the driver's state (fatigue, sleepiness, and inattention), which can reduce accidents and improve the public safety (Azim et al 2014;Bergasa and Nuevo 2006;Ji et al 2004;Jin et al 2013). Nowadays, ITS community is giving wide attention to the development of autonomous cars, but despite the progress of these researches, the trust to adopt the driving of fully autonomous cars is not high among many potential users (older adults), whereas greater than 50% of these users prefer helper cars (Abraham et al 2016), which can be considered as countermeasures when driver's state is unsafe.…”

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confidence: 99%

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Feature selection for driving fatigue characterization and detection using visual- and signal-based sensors

Henni

Mezghani²,

Vallerand

et al. 2018

Appl Inform

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Nowadays, about 1.25 million people die each year as a result of road traffic crashes (World Health Organization WHO). Without sustained action, road traffic crashes are predicted to become the leading cause of death by 2030, including among the growing population of senior drivers. 10-20% of all traffic accidents are made by nonvigilant drivers and 60% of these accidents are due to fatigue (Awake Consortium 2001). Fatigue is defined as a transitional state between awakenings and sleeping. It affects the skills required for a safe driving, by increasing the driving error frequency as well as their amplitudes and variability. It also reduces the driver perception and decision-making capability to control the vehicle (Sahayadhas et al. 2012). Fatigue depends on several endogenous and exogenous factors, for example, age, motivation and driving time. Otmani et al. (2005) demonstrated that drivers are fatigued after 20 min of driving, and countermeasures must be considered before the driver reaches

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Feature selection for driving fatigue characterization and detection using visual- and signal-based sensors

Henni

Mezghani²,

Vallerand

et al. 2018

Appl Inform

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“…The data output includes over 145 values covering, among others, gaze, eyelid, pupilometry and head tracking, raw and filtered gaze, blinks, fixations and saccades. Smart Eye has been used in several driver assistance and inattention systems, such as [76,143,144,145,238]. …”

Section: Sensorsmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

101

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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“…Numerous previous studies have shown that the duration of continuous driving without rest was signi cantly associated with the deterioration of the driving performance [9][10][11][12]. Accordingly, many researchers have focused their attention on identifying the symptoms and level of sleepiness experienced by the driver while driving, among which visual behaviour variables are the most frequently used measures [13,14]. They found that sleepy drivers exhibit certain observable visual changes, such as slow eyelid movement, small degree of eye opening (or even closed), longer blink time, frequent nodding, gaze, etc., which can be used to detect the level of sleepiness [7,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

How does long trip affect on driving performance, eye blinks and awareness of sleepiness among commercial drivers? A naturalistic driving test study

Wang

Wei

2018

Scientia Iranica

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Abstract. A total of 120 commercial drivers from four age groups participated in the expressway driving test in Shandong, China, to perform continuous driving tasks of 2, 3, and 4 h and collect the data on the driver's blinking, driving performance, and self-reported level of sleepiness. A two-way repeated measures ANOVA was used to evaluate the e ects of driving duration on the variation of the eye-blink behavior, driving performance, and subjective feeling of sleepiness across the di erent age groups over the time periods tested. Additionally, Pearson product-moment correlation was used to quantify the association between the variations of the dependent variables. The results showed that there was signi cant di erence between groups, signi cant e ect over time, and signi cant interaction between the age and driving duration in the variations of the driver's blink frequency, blink duration, closure duration, speed perception, choice reaction time, the number of incorrect action judgments, and subjective level of sleepiness. However, a signi cant di erence varied over time, yet no e ect of the interaction between groups and time was found in the variation of the driver's attention allocation value. Furthermore, driver's eye blink measures were more sensitive to sleepiness, and older drivers were more likely to get sleepy in long distance driving.

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Driver Sleepiness Detection System Based on Eye Movements Variables

Cited by 32 publications

References 31 publications

Feature selection for driving fatigue characterization and detection using visual- and signal-based sensors

Feature selection for driving fatigue characterization and detection using visual- and signal-based sensors

Driver Distraction Using Visual-Based Sensors and Algorithms

How does long trip affect on driving performance, eye blinks and awareness of sleepiness among commercial drivers? A naturalistic driving test study

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