Driver Drowsiness Detection based on Monitoring of Eye Blink Rate

P, Baby Shamini; Vinodhini, M.; Keerthana, B; Lakshna, S; Meenatchi, K. R

doi:10.1109/icssit53264.2022.9716304

Cited by 8 publications

(2 citation statements)

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“…The eye status is gotten through picture handling calculations. This paper considers just the condition of the eyes, it doesn't zero in on the recurrence of yawning [7]..In this framework PC vision and liquor gas sensor blend is utilized to identify tiredness and liquor inebriation. This framework utilizes Raspberry-pi and Arduino UNO with I2C convention.…”

Section: Literature Overviewmentioning

confidence: 99%

A Novel Approach to Driver Drowsiness Detection System Using Neural Networks

2022

IRJMETS

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Sleepiness of the drivers is the primary driver of mishaps on the planet as well as Sri Lanka. Due to absence of rest and sluggishness, tiredness can happen while driving. The most effective way to stay away from mishaps brought about by drivers' sleepiness is to recognize tiredness of the driver and caution him before fall into rest. To recognize sleepiness numerous methods like eye retina location, facial component acknowledgment has been used. The need is on working on the wellbeing of the driver without being prominent. In this undertaking the eye flicker of the driver is distinguished. On the off chance that the drivers eyes stay shut for more than a certain timeframe, the driver is supposed to be tired and an alert is sounded. An endless number of individuals drive on the expressway constantly. Cabbies, transport drivers, transporters and individuals voyaging significant distance experience the ill effects of absence of rest. Because of which it turns out to be extremely perilous to drive while feeling sleepy. The greater part of mishaps occur because of the tiredness of the driver. Thus, to forestall these mishaps we will fabricate a framework utilizing Python, OpenCV, and Keras which will caution the driver when he feels sluggish.

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Section: Literature Overviewmentioning

confidence: 99%

A Novel Approach to Driver Drowsiness Detection System Using Neural Networks

2022

IRJMETS

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“…In this regard, several parameters can be considered to monitor drivers' state. These include Eyeblink Rate (EBR) (Baby Shamini et al, 2022;Rahman et al, 2015), Heart Rate (HR) (Fujiwara et al, 2019;Sensors, 2023), Electroencephalography (EEG) (Houshmand et al, 2021;Simon et al, 2011) and driving parameters (PMC, 2023).…”

Section: Introductionmentioning

confidence: 99%

Multimodal characterization of mental fatigue on professional drivers

Giorgi¹,

Ronca²,

Vozzi³

et al. 2023

Neuroergonomics and Cognitive Engineering

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A non-adequate psychophysical condition represents a major factor in causing car accidents. In particular, 20% of car crashes are caused by mental fatigue and drowsiness, with dramatic consequences and fatalities. Nowadays strategies to reduce the risks while driving. The on-board systems equipping current vehicles are not able to intervene before the sudden onset of drowsy episodes and are affected by a poor accuracy resulting in several events’ misclassifications (i.e., false positive) causing drivers’ mistrust of technology.Being able to recognise in advance the occurrences of fatigue and drowsiness episodes would dramatically increase road safety and reduce car crashes. This is extremely relevant especially for professional drivers who drive for prolonged periods leading to an increase of risks due to not-proper psychophysical conditions. Even if professional drivers are trained to prevent, recognise, and minimize the effect of fatiguing, it must be considered that often the driver becomes conscious of drowsiness and mental fatigue onset too late, that is when it is already driving in a not-safe condition. The aim of this study was to adopt a multimodal approach to characterize the initial phases of fatigued mental state while driving, to develop an effective and timely detecting methodology. Ten volunteer professional drivers have been recruited to take part in an experimental protocol, performed in a car simulator. The experiment took place in the afternoon to increase the chance of eliciting mental fatigue and it consisted in driving for 45 minutes in a monotonous city-like environment. Before performing the monotonous driving task, participants were asked to drive for 15 minutes in a high-difficulty track race to induce fatigue, increasing the probability of a not-adequate psychophysical condition during the following monotonous driving task. Aiming at developing a neurophysiological model for mental fatigue characterization, a multimodal neurophysiological assessment was performed collecting the Electroencephalographic (EEG) and Electroculographic (EOG) signals. In parallel, behavioural assessment was performed through a secondary reaction task to detect eventual variation of performance from individual normal levels because of an altered psychophysical condition. Subjective measures were collected as well for the self-assessment of both fatigue and drowsiness state and task perception (high vs low demand). Behavioural and subjective measures have been so employed to (i) validate the experimental design; and to (ii) support and validate the employment of neurophysiological measures for characterizing the mental fatigue.

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Enhancing Driver Safety and Experience: Real-Time Facial Expression Detection in Smart Vehicles with Vision Systems

Mishra,

Mahapatra

et al. 2024

Proceedings of 4th International Conference on Frontiers in Computing and Systems

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Driver Drowsiness Detection based on Monitoring of Eye Blink Rate

Cited by 8 publications

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A Novel Approach to Driver Drowsiness Detection System Using Neural Networks

A Novel Approach to Driver Drowsiness Detection System Using Neural Networks

Multimodal characterization of mental fatigue on professional drivers

Enhancing Driver Safety and Experience: Real-Time Facial Expression Detection in Smart Vehicles with Vision Systems

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