Drowsy and Fatigued Driver Warning, Counter Measures, and Assistance

Sayed, Riaz Akbar

doi:10.1007/978-0-85729-085-4_37

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…A few of the past studies on the evaluation, classification, or prediction of drowsy states have attempted to predict the point in time with high likelihood of falling asleep; however, as pointed out by Sayed et al [27], no definite and effective methods that can determine when the alarm should be presented to the driver exist. It is possible to predict the psychological rating on drowsiness using behavioral and physiological measures [24,28,29].…”

Section: Prediction Of Point In Time With High Crash Risk By Integratmentioning

confidence: 99%

“…The lower value of EEG-MPF shows that the arousal level is decreased. EEG-α/β can be calculated as the ration of the sum of α-band (8-12 Hz) power and θ-band (4-7 Hz) power to the power of β-band (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) power. The higher this value is, the lower the arousal level gets.…”

Section: Calculation Of the Integrated Posterior Probabilities P(h 1 mentioning

confidence: 99%

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Untitled

2016

JTTE

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Abstract:The aim of this study was to predict drivers' drowsy states with high risk of encountering a crash and prevent drivers from continuing to drive under such drowsy states with high risk of crash. While the participants were required to carry out a simulated driving task, EEG (Electroencephalography) (EEG-MPF and EEG-α/β), ECG (Electrocradiogram) (RRV3), tracking error, and subjective rating on drowsiness were measured. On the basis of such measurements, an attempt was made to predict the point in time with high crash risk using Bayesian estimation of posterior probability of drowsiness, tracking error, and subjective drowsiness. As a result of applying the proposed method to the data of each participant, it was verified that the proposed method could predict the point in time with high crash risk before the point in time of crash.

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Section: Prediction Of Point In Time With High Crash Risk By Integratmentioning

confidence: 99%

Section: Calculation Of the Integrated Posterior Probabilities P(h 1 mentioning

confidence: 99%

Untitled

2016

JTTE

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“…In the United State, 37% of drivers that drive in the morning after night shift experienced a near-crash incident (6). However, a warning system device will still have a problem if it is not developed properly and the alarm did not represent the true state of drowsiness drivers (17). The study will contribute to a new finding to understand the driver's heart rate response and behaviour.…”

Section: Introductionmentioning

confidence: 98%

Monitoring Driver’s Heart Rate Response Using Heart Rate Detection Device (HDD)

Makhtar¹,

Khalim²,

Wahab³

et al. 2022

MJMHS

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Introduction: Drowsiness driving is one factor contributing to road accidents resulting in fatalities of drivers and passengers. However, a warning system device to alert drivers about the drowsiness level has never been appropriately developed. This study aims to create a heart rate detection device that monitors drivers’ heart rate and notify them about their drowsiness, which will, in the long term can help to reduce the number of road accidents due to drowsiness. Methods: In this experimental research, ten participants (Mean Age= 24 Years-Old, SD=0.4) were attached to the developed heart rate detection device on the steering wheel of a driving simulator. The participants underwent four 30-min sessions on the driving simulator sessions and heart rate reading throughout the driving period to obtain the heart rate mean value from the first three sessions. The obtained heart rate mean value was set as a threshold value so that the alarm will trigger if the value of heart rate falls below the threshold value in the fourth session. Results: The results showed that the respondents’ average heart rate (bpm) decreased gradually the longer time they drove the car simulator. This study also revealed that the heart rate detection device successfully monitored the driver’s heart rate and notified them when they were at drowsiness level. Conclusion: Overall, the device should be more user friendly by improving the sensitivity of sensors in all parts of the steering wheel for better data collection.

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“…Other studies have assessed, classified and predicted drowsiness states ( Ji et al, 2004 , 2006 ; Sayed et al, 2012 ; Singh and Banga, 2013 ; Kusuma and Sunitha, 2014 ; Samiee et al, 2014 ). However, no definite or effective methods emerged from this research for determining when a warning signal should be presented to the driver.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Driver’s Drowsiness Based on Fractal Dimensional Analysis of Sitting and Back Pressure Measurements

2018

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The most effective way of preventing motor vehicle accidents caused by drowsy driving is through a better understanding of drowsiness itself. Prior research on the detection of symptoms of drowsy driving has offered insights on providing drivers with advance warning of an elevated risk of crash. The present study measured back and sitting pressures during a simulated driving task under both high and low arousal conditions. Fluctuation of time series of center of pressure (COP) movement of back and sitting pressure was observed to possess a fractal property. The fractal dimensions were calculated to compare the high and low arousal conditions. The results showed that under low arousal (the drowsiness state) the fractal dimension was significantly lower than what was calculated with high arousal. Accumulated drowsiness thus contributed to the loss of self-similarity and unpredictability of time series of back and sitting pressure measurement. Drowsiness further reduces the complexity of the posture control system as viewed from back and sitting pressure. Thus, fractal dimension is a necessary and sufficient condition of a decreased arousal level. It further is a necessary condition for detecting the interval or point in time with high risk of crash.

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Drowsy and Fatigued Driver Warning, Counter Measures, and Assistance

Cited by 5 publications

References 33 publications

Untitled

Untitled

Monitoring Driver’s Heart Rate Response Using Heart Rate Detection Device (HDD)

Assessment of Driver’s Drowsiness Based on Fractal Dimensional Analysis of Sitting and Back Pressure Measurements

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