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

Murata, Atsuo; Fukuda, Kohei

doi:10.1080/15389588.2015.1050720

Cited by 4 publications

(6 citation statements)

References 8 publications

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“…However, detecting at least the appearance of drowsiness still may be a first step toward detecting the interval or point in time with high risk of crash. If fractal dimension significantly decreases, we proceed to the second step for detecting the interval or point in time with high risk of crash using the method by Murata (2016) , Murata and Fukuda (2016) , Murata et al (2017) , and Murata (2018) .…”

Section: Discussionmentioning

confidence: 99%

“…Fractal dimension’s changes over long time intervals may shed light on the detection of drowsy driving. It also may enhance our ability to identify a state with high risk of crash if used together with the techniques already developed by Murata (2016) , Murata and Fukuda (2016) , Murata et al (2017) , and Murata (2018) . Furthermore, a detection of a decrease in fractal dimension over a fixed interval may reveal the point in time or interval with high risk of crash using the methods proposed by Murata (2016) , Murata and Fukuda (2016) , Murata et al (2017) , and Murata (2018) .…”

Section: Discussionmentioning

confidence: 99%

“…Among vehicle-based measures are lane position, line crossing and steering wheel inputs. Back, foot and sitting pressure are classified as behavioral measures ( Murata et al, 2013 ; Murata, 2016 ; Murata and Fukuda, 2016 ). Behavioral and vehicle-based measures possess the practical advantage of noninvasive measurement relative to physiological measurement.…”

Section: Introductionmentioning

confidence: 99%

“… Murata (2016) , Murata and Fukuda (2016) , Murata et al (2017) , and Murata (2018) employed an X -bar control chart or Bayesian theory in a simulated driving task. In doing so they succeeded to some extent in identifying the point in time of high risk of crash before a virtual crash actually occurred.…”

Section: Introductionmentioning

confidence: 99%

“…The approaches taken in Murata (2016) , Murata and Fukuda (2016) , Murata et al (2017) , and Murata (2018) require careful and continuing tracking of changes to identify the high risk point in time. This aspect of these studies makes it more difficult to assess accumulated drowsiness.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

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A method for predicting the risk of virtual crashes in a simulated driving task using behavioural and subjective drowsiness measures

2016

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This study proposed a procedure for predicting the point in time with high risk of virtual crash using a control chart methodology for behavioural measures during a simulated driving task. Tracking error, human back pressure, sitting pressure and horizontal and vertical neck bending angles were measured during the simulated driving task. The time with a high risk of a virtual crash occurred in 9 out of 10 participants. The time interval between the successfully detected point in time with high risk of virtual crash and the point in time of virtual crash ranged from 80 to 324 s. The proposed procedure for predicting the point in time with a high risk of a crash is promising for warning drivers of the state of high risk of crash. Practitioner Summary: Many fatal crashes occur due to drowsy driving. We proposed a method to predict the point in time with high risk of virtual crash before such a virtual crash occurs. This is done using behavioural measures during a simulated driving task. The effectiveness of the method is also demonstrated.

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Risk factors for being the at-fault driver: A case–control study

Fararouei

Sedaghat

Sadat

et al. 2017

Traffic Injury Prevention

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

Abstract: The proposed method is a promising technique for predicting in advance the time zone with potentially high risk (probability) of being involved in an accident due to drowsy driving and for warning drivers of such a drowsy and risky state.

Cited by 4 publications

References 8 publications

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

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

A method for predicting the risk of virtual crashes in a simulated driving task using behavioural and subjective drowsiness measures

Risk factors for being the at-fault driver: A case–control study

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