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

Atsuo Murata, Kensuke Naitoh, Waldemar Karwowski

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalErgonomics
DOIs
Publication statusAccepted/In press - Aug 28 2016

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Sleep Stages
Pressure
human error
time
Neck
driver
methodology

Keywords

  • Automotive crash risk
  • behavioural measures
  • psychological rating of drowsiness
  • virtual crash
  • X-bar control chart

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Physical Therapy, Sports Therapy and Rehabilitation

Cite this

A method for predicting the risk of virtual crashes in a simulated driving task using behavioural and subjective drowsiness measures. / Murata, Atsuo; Naitoh, Kensuke; Karwowski, Waldemar.

In: Ergonomics, 28.08.2016, p. 1-17.

Research output: Contribution to journalArticle

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