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

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.

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

“…The experimental system consisted of a display of a simulated driving task and a steering wheel used for the task. The detailed display of the inside lane was the same as that used by Murata et al (2013 , 2017 ). It consisted of three lanes, each of which was 3.6 m wide.…”

“…Each participant sat on an automobile seat to perform the simulated driving task (For more detail, refer to Murata et al, 2013 , 2017 ). The task required participants to follow a preceding car while maintaining a moderate distance between the preceding car and their own car.…”

“…The point in time of virtual crash was identified on the basis of the tracking error in the main driving task and the observation of behavior of participant according to the criteria in Murata et al (2017) . Two experimenters monitored the participants’ state to ensure objectivity and consistency in the judgment process.…”

“… 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.…”

“…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.…”