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

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.

研究目的：本文旨在探究并对比驾驶员在分心驾驶启动前后的行为频域及时域特征。
研究方法：本研究采用已有的自然驾驶研究（naturalistic driving study）数据集。采用快速傅里叶变换（Fast Fourier Transform，FFT）开展频域分析，以探究驾驶员在非分心（视觉-手动任务（visual-manual task）启动前）与分心（视觉-手动任务启动后）驾驶阶段的行为模式变化。针对车辆控制变量（即车道偏移量、横摆角速度及加速度），计算其在0~0.5Hz低频区间内的平均相对频谱功率，以及10秒时间窗口内的标准差，并进行对比分析。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：时域与频域分析的混合模型结果均显示，驾驶员在驾驶过程中执行视觉-手动任务后，其横向操控性能出现显著下降。敏感性分析结果表明，频域分析对频带宽度的敏感性较低，而时域分析对变异计算所选的时间间隔更为敏感。不同的时间间隔选择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带内的平均频谱功率分析则得到一致结果：相较于非分心驾驶阶段，分心驾驶阶段的变异水平更高。
研究结论：本研究表明，驾驶员状态检测需考虑分心启动前阶段的行为变化，而非仅关注如使用手机这类存在明显分心行为的时段。相较于纵向控制指标，横向控制指标可作为更优的分心检测标识。此外，相较于时域分析方法，频域分析在评估驾驶性能时展现出更强的稳健性与一致性。