Wang Yuan, Bao Shan, Du Wenjun, Ye Zhirui, Sayer James R
a University of Michigan Transportation Research Institute , Ann Arbor , Michigan.
b School of Transportation , Southeast University , Nanjing , Jiangsu , China.
Traffic Inj Prev. 2017 Nov 17;18(8):826-831. doi: 10.1080/15389588.2017.1320549. Epub 2017 May 23.
This article investigated and compared frequency domain and time domain characteristics of drivers' behaviors before and after the start of distracted driving.
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 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.
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.
本文研究并比较了分心驾驶开始前后驾驶员行为的频域和时域特征。
使用了一项现有的自然驾驶研究数据。应用快速傅里叶变换（FFT）进行频域分析，以探索在非分心（视觉手动任务开始前）和分心（视觉手动任务开始后）驾驶期间驾驶员行为模式的变化。计算了低频范围（0 - 0.5赫兹）内的平均相对谱功率以及车辆控制变量（即车道偏移、横摆率和加速度）在10秒时间窗口内的标准差，并进行了进一步比较。还进行了敏感性分析，以检验时域和频域分析的可靠性。
时域和频域分析的混合模型分析结果均显示，驾驶时进行视觉手动任务后横向控制性能显著下降。敏感性分析结果表明，频域分析对频率带宽不太敏感，而时域分析对用于变化计算的时间间隔更敏感。不同的时间间隔选择会导致标准差数值有显著差异，而在低频和高频带宽上对横摆率的平均谱功率分析显示结果一致，即与非分心驾驶相比，分心驾驶期间观察到更高的变化值。
本研究表明，驾驶员状态检测需要考虑开始前阶段的行为变化，而不是仅关注存在分心行为的阶段，如使用手机时。横向控制措施可能比分纵向控制措施更适合作为分心检测指标。此外，与时域分析相比，频域分析在评估驾驶性能方面被证明是一种更稳健、更一致的方法。
