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Abstract
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OBJECTIVE: This paper investigated and compared frequency-domain and time-domain characteristics of drivers' behavior 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 non-distracted (pre-starting of visual-manual task) and distracted (post-starting 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 while the time domain analysis was more sensitive to the time intervals selected for variation calculations. Different time intervals selections can result in significantly different standard deviation values, while average spectral power analysis on yaw rate in both low and high frequency bandwidths showed consistently results, that higher variation values were observed during distracted driving when compared to non-distracted driving.
CONCLUSIONS: This study suggests that driver state detection needs to consider the behavior changes during the pre-starting 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 performances, when compared to time domain analyses.
Language: en |