Prediction of Drowsy Driving Using Behavioral Measures of Drivers – Change of Neck Bending Angle and Sitting Pressure Distribution

Murata, Atsuo; Endoh, Takuya; Hayami, Takehito

doi:10.1007/978-3-642-39173-6_10

Cited by 4 publications

(9 citation statements)

References 3 publications

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“…Although Murata et al [29] proposed a method to calculate a posterior probability of drowsiness P(H 1 |X), they did not show a concrete procedure for predicting the point in time with high crash risk that might lead to a crash if the participant continues a simulated driving task. They also did not define and identify the point in time of virtual crash.…”

Section: Discussionmentioning

confidence: 99%

“…Using physiological measures such as EEG-MPF, EEG-α/β-ratio, and RRV3 is not practical and feasible due to high price (cost) of such physiological measurement apparatus. Thus, it should be explored whether the application of the proposed method to only the behavioral drowsiness evaluation measure [20,21,29] can also effectively predict the point in time with high crash risk.…”

Section: Discussionmentioning

confidence: 99%

“…It is possible to predict the psychological rating on drowsiness using behavioral and physiological measures [24,28,29]. However, currently it is impossible to predict the point in time with high crash risk (crash in simulated driving) before such a crash occurs.…”

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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Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

Murata¹,

Uragami²

2018

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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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%

See 2 more Smart Citations

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

Murata¹,

Uragami²

2018

JTTE

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“…However, this study did not make an attempt to predict the arousal level. Murata et al [7] and Murata et al [8] applied a logistic regression model to mainly physiological measures such as EEG, ECG or EOG (electrooculography) in order to predict the arousal level (the subjective rating on drowsiness) and attained a prediction accuracy of about 85%.…”

mentioning

confidence: 99%

Multinomial Logistic Regression Model for Predicting Driver’s Drowsiness Using Only Behavioral Measures

Murata¹,

Naitoh²

2015

JTTE

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Abstract:The aim of this study was to explore the effectiveness of behavioral evaluation measures for predicting drivers' subjective drowsiness. Behavioral measures included neck bending angle, back pressure, foot pressure, COP (center of pressure) movement on sitting surface and tracking error in driving simulator task. Drowsy states were predicted by means of the multinomial logistic regression model where behavioral measures and subjective evaluation of drowsiness corresponded to independent variables and a dependent variable, respectively. First, we compared the effectiveness of two methods (correlation coefficient-based method and odds ratio-based method) for determining the order of entering behavioral measures into the prediction model. It was found that the prediction accuracy did not differ between both methods. Second, the prediction accuracy was compared among the numbers of behavioral measures. The prediction accuracy did not differ among four, five and six behavioral measures and it was concluded that entering at least four behavioral measures into the prediction model is enough to achieve higher prediction accuracy. Third, the prediction accuracy was compared between the strongly drowsy and the weakly drowsy groups. The prediction accuracy differed between the two groups and the proposed method was effective under the condition where drowsiness was induced to a larger extent.

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Development of a method to predict crash risk using trend analysis of driver behavior changes over time

Murata

Fukuda

2015

Traffic Injury Prevention

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Prediction of Drowsy Driving Using Behavioral Measures of Drivers – Change of Neck Bending Angle and Sitting Pressure Distribution

Cited by 4 publications

References 3 publications

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

Prediction of Point in Time with High Crash Risk by Integration of Bayesian Estimation of Drowsiness, Tracking Error, and Subjective Drowsiness

Multinomial Logistic Regression Model for Predicting Driver’s Drowsiness Using Only Behavioral Measures

Development of a method to predict crash risk using trend analysis of driver behavior changes over time

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