A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction. (17th November 2017)

• Record Type: Journal Article
• Authors: Wang, Yuan; Bao, Shan; Du, Wenjun; Ye, Zhirui; Sayer, James R.
• Is Part Of: Traffic injury prevention. Volume 18:Number 8(2017)
• Page Start: 826
• Page End: 831
• Publication Date: 2017-11-17
• Subjects: Distracted driving behavior -- fast Fourier transform -- naturalistic driving study data -- vehicle control measures -- behavior change; Traffic safety -- Periodicals; Traffic accidents -- Periodicals; Wounds and injuries -- Prevention -- Periodicals; 363.125
• DOI: 10.1080/15389588.2017.1320549
• Languages: English
• ISSNs: 1538-9588

Abstract

ABSTRACT: Objective : This article investigated and compared frequency domain and time domain characteristics of drivers' behaviors before and after the start of distracted driving. Method : Data from an existing naturalistic driving study were used. Fast Fourier transform (FFT) was applied for the frequency domain analysis to explore drivers' behavior pattern changes between nondistracted (prestarting of visual–manual task) and distracted (poststarting of visual–manual task) driving periods. Average relative spectral power in a low frequency range (0–0.5 Hz) and the standard deviation in a 10-s time window of vehicle control variables (i.e., lane offset, yaw rate, and acceleration) were calculated and further compared. Sensitivity analyses were also applied to examine the reliability of the time and frequency domain analyses. Results : Results of the mixed model analyses from the time and frequency domain analyses all showed significant degradation in lateral control performance after engaging in visual–manual tasks while driving. Results of the sensitivity analyses suggested that the frequency domain analysis was less sensitive to the frequency bandwidth, whereas the time domain analysis was more sensitive to the time intervals selected for variation calculations. Different time interval selections can result in significantly different standard deviation values, whereas average spectral power analysis on yaw rate in both low and high frequency bandwidths showed consistent results, that higher variation values were observed during distracted driving when compared to nondistracted driving. Conclusions : This study suggests that driver state detection needs to consider the behavior changes during the prestarting periods, instead of only focusing on periods with physical presence of distraction, such as cell phone use. Lateral control measures can be a better indicator of distraction detection than longitudinal controls. In addition, frequency domain analyses proved to be a more robust and consistent method in assessing driving performance compared to time domain analyses.