Adaptability to ambient light changes for drowsy driving detection using image processing

PurposeTo identify and appraise the published studies assessing interventions accounting for reducing fatigue and sleepiness while driving.MethodsThis systematic review searched the following electronic databases: Medline, Science direct, Scopus, EMBASE, PsycINFO, Transport Database, Cochrane, BIOSIS, ISI Web of Knowledge, specialist road injuries journals and the Australian Transport and Road Index database. Additional searches included websites of relevant organizations, reference lists of included studies, and issues of major injury journals published within the past 15 years. Studies were included if they investigated interventions/exposures accounting for reducing fatigue and sleepiness as the outcome, measured any potential interventions for mitigation of sleepiness and were written in English. Meta-analysis was not attempted because of the heterogeneity of the included studies.ResultsOf 63 studies identified, 18 met the inclusion criteria. Based on results of our review, many interventions in the world have been used to reduce drowsiness while driving such as behavioral (talking to passengers, face washing, listening to the radio, no alcohol use, limiting the driving behavior at the time of 12 p.m. – 6 a.m. etc), educational interventions and also changes in the environment (such as rumble strips, chevrons, variable message signs, etc). Meta-analysis on the effect of all these interventions was impossible due to the high heterogeneity in methodology, effect size and interventions reported in the assessed studies.ConclusionResults of present review showed various interventions in different parts of the world have been used to decrease drowsy driving. Although these interventions can be used in countries with high incidence of road traffic accidents, precise effect of each intervention is still unknown. Further studies are required for comparison of the efficiency of each intervention and localization of each intervention according to the traffic patterns of each country.

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“…For further information, definition of these measurement scales was summarized in Table 1 . 11 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22…”

Section: Methodsmentioning

confidence: 99%

“…The Copilot uses a structured illumination approach to identifying a driver's eyes”. 18 Electro Dermal Activity 19 “EDA (Electro-dermal Activity) signal is an electric response on the skin of the human body. This system, for example, can be used in detecting and preventing drowsiness driving accidents for automobile drivers”.…”

Section: Methodsmentioning

confidence: 99%

A systematic review of the effect of various interventions on reducing fatigue and sleepiness while driving

Nazari

Moradi

Rahmani

2017

Chinese Journal of Traumatology

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“…[12] uses infrared sensors monitoring the driver's head movement to detect drowsy driving. [13] captures the driver's facial images using a camera to detect whether the driver is drowsy driving by image processing. In [4], GPS, cameras, alcohol sensor and accelerometer sensor are used to detect driver's status of drunk, fatigued, or reckless.…”

Section: Related Workmentioning

confidence: 99%

D<sup>3</sup>: Abnormal driving behaviors detection and identification using smartphone sensors

Chen

Zhu

et al. 2015

2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarsegrained result, i.e. distinguishing abnormal driving behaviors from normal ones. To improve drivers' awareness of their driving habits so as to prevent potential car accidents, we need to consider a finegrained monitoring approach, which not only detects abnormal driving behaviors but also identifies specific types of abnormal driving behaviors, i.e. Weaving, Swerving, Sideslipping, Fast Uturn, Turning with a wide radius and Sudden braking. Through empirical studies of the 6-month driving traces collected from real driving environments, we find that all of the six types of driving behaviors have their unique patterns on acceleration and orientation. Recognizing this observation, we further propose a finegrained abnormal Driving behavior Detection and iDentification system, D 3 , to perform real-time high-accurate abnormal driving behaviors monitoring using smartphone sensors. By extracting unique features from readings of smartphones' accelerometer and orientation sensor, we first identify sixteen representative features to capture the patterns of driving behaviors. Then, a machine learning method, Support Vector Machine (SVM), is employed to train the features and output a classifier model which conducts fine-grained identification. From results of extensive experiments with 20 volunteers driving for another 4 months in real driving environments, we show that D 3 achieves an average total accuracy of 95.36%.

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Vigilance monitoring for operator safety: A simulation study on highway driving

Desai

Haque

2006

Journal of Safety Research

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Adaptability to ambient light changes for drowsy driving detection using image processing

Cited by 5 publications

References 0 publications

A systematic review of the effect of various interventions on reducing fatigue and sleepiness while driving

A systematic review of the effect of various interventions on reducing fatigue and sleepiness while driving

D<sup>3</sup>: Abnormal driving behaviors detection and identification using smartphone sensors

Vigilance monitoring for operator safety: A simulation study on highway driving

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