A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction

<b>Objective</b>: This article investigated and compared frequency domain and time domain characteristics of drivers' behaviors before and after the start of distracted driving. <b>Method</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.

**研究目标**：本文针对驾驶员在分心驾驶启动前后的行为特征，分别从频域与时域维度展开分析并进行对比。
**研究方法**：本研究采用现有自然驾驶研究的数据集。针对频域分析，本研究采用快速傅里叶变换（Fast Fourier Transform, FFT），探究驾驶员在非分心（视觉-手动任务启动前）与分心（视觉-手动任务启动后）驾驶阶段的行为模式差异。研究计算并对比了车辆控制变量（即车道偏移量、横摆角速度与加速度）在低频段（0–0.5 Hz）的平均相对谱功率，以及10秒时间窗口内的标准差。此外还开展了敏感性分析，以检验时域与频域分析结果的可靠性。
**研究结果**：时域与频域的混合模型分析结果均显示，驾驶员在执行视觉-手动任务后，其横向控制性能出现显著下降。敏感性分析结果表明，频域分析对频段选择的敏感性更低，而时域分析对变异计算所用的时间区间选取更为敏感。不同的时间区间选取会导致标准差结果出现显著差异；但针对横摆角速度在高低频段的平均谱功率分析则得到一致结论：相较于非分心驾驶阶段，分心驾驶阶段的变异程度更高。
**研究结论**：本研究指出，驾驶员状态检测需考虑分心启动前的行为变化，而非仅关注实际发生分心行为的阶段（如使用手机）。横向控制指标用于分心检测的效果优于纵向控制指标。此外，相较于时域分析方法，频域分析在评估驾驶性能时表现出更强的稳健性与一致性。

搜集汇总

背景与挑战

背景概述

该数据集通过频域和时域分析方法，研究了驾驶行为在分心前后的变化特征，发现分心驾驶会显著降低横向控制性能，且频域分析方法比时域分析更具鲁棒性。数据集包含车辆控制变量的分析结果，为分心驾驶检测提供了重要参考。