Drunk driving and drowsiness detection

Charniya, Nadir N.; Nair, Vivek

doi:10.1109/i2c2.2017.8321811

Cited by 19 publications

(4 citation statements)

References 10 publications

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“…This prominence is primarily attributed to the strong correlation between driver drowsiness and car accidents. The two primary causes of road accidents and the resulting financial losses are driving while drowsy or in a sleepy condition [4]. The detrimental impact of driver fatigue, resulting in a loss of concentration, significantly impairs the car driver's ability to make timely and effective decisions [5].…”

Section: Drowsiness Detection Systemmentioning

confidence: 99%

Enhancing Driver Safety: Real-Time Eye Detection for Drowsiness Prevention Driver Assistance Systems

Zain,

Roseli,

Abdullah

2023

Ieec 2023

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Section: Drowsiness Detection Systemmentioning

confidence: 99%

Enhancing Driver Safety: Real-Time Eye Detection for Drowsiness Prevention Driver Assistance Systems

Zain,

Roseli,

Abdullah

2023

Ieec 2023

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“…To solve the issue of DUI, the literature commonly suggests the use of ignition-interlock devices [ 218 , 235 , 236 ]. When a driver enters his/her vehicle, he/she must provide a breath sample, and an alcohol sensor then determines whether he/she is drunk (that is, has a BAC above a specified threshold).…”

Section: State 5: Under the Influencementioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

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“…This is mainly due to the correlation of driver drowsiness with car accidents. Driving while being drowsy or in sleepy condition, are the two primary causes of road accidents and the related financial losses [10]. Seeing that loss of concentration as a consequence of driver's fatigue prevents the car driver from taking a right and rapid decision [11].…”

Section: Drowsiness Detection Systemmentioning

confidence: 99%

Computer Vision Based Driver Assistance Drowsiness Detection

Emashharawi

Khalifa

Malik

et al. 2021

Lecture Notes in Electrical Engineering

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Nowadays, drowsiness is a serious cause of traffic accidents, a problem of major concern to society. Driver fatigue or sleepiness decreases the driver's reaction time, reduces attention, and affects the quality of decision making which impairs the driving experience. Therefore, in this paper, a drowsiness detection system is designed based on computer vision, using a cascade of classifiers based on Haar-like features. The system is able to detect the face and eyes of the driver and determine the eyes closure or opening, which concludes the drowsiness of the driver. The paper presents the five primary steps involves which are: video acquirement, frame separation, face detection, eyes detection and drowsiness detection.

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Drunk driving and drowsiness detection

Cited by 19 publications

References 10 publications

Enhancing Driver Safety: Real-Time Eye Detection for Drowsiness Prevention Driver Assistance Systems

Enhancing Driver Safety: Real-Time Eye Detection for Drowsiness Prevention Driver Assistance Systems

Survey and Synthesis of State of the Art in Driver Monitoring

Computer Vision Based Driver Assistance Drowsiness Detection

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