Literature DB >> 27571847

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

Atsuo Murata1, Kensuke Naitoh1, Waldemar Karwowski2.   

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.

Entities:  

Keywords:  Automotive crash risk; X-bar control chart; behavioural measures; psychological rating of drowsiness; virtual crash

Mesh:

Year:  2016        PMID: 27571847     DOI: 10.1080/00140139.2016.1223885

Source DB:  PubMed          Journal:  Ergonomics        ISSN: 0014-0139            Impact factor:   2.778


  1 in total

1.  Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection : SEEGDD.

Authors:  Venkata Phanikrishna Balam; Suchismitha Chinara
Journal:  Phys Eng Sci Med       Date:  2021-05-31
  1 in total

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