Wang Fuwang, Wang Hao, Zhou Xin, Fu Rongrong
School of Mechanic Engineering, Northeast Electric Power University, Jilin City 132012, China.
College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Brain Sci. 2022 Sep 6;12(9):1199. doi: 10.3390/brainsci12091199.
Driving fatigue refers to a phenomenon in which a driver's physiological and psychological functions become unbalanced after a long period of continuous driving, and their driving skills decline objectively. The hidden dangers of driving fatigue to traffic safety should not be underestimated. In this work, we propose a judgment excitation mode (JEM), which adds secondary cognitive tasks to driving behavior through dual-channel human-computer interaction, so as to delay the occurrence of driving fatigue. We used multifractal detrended fluctuation analysis (MF-DFA) to study the dynamic properties of subjects' EEG, and analyzed the effect of JEM on fatigue retardation by Hurst exponent value and multifractal spectrum width value. The results show that the multifractal properties of the two driving modes (normal driving mode and JEM) are significantly different. The JEM we propose can effectively delay the occurrence of driving fatigue, and has good prospects for future practical applications.
驾驶疲劳是指驾驶员在长时间连续驾驶后生理和心理功能失衡，其驾驶技能客观下降的一种现象。驾驶疲劳对交通安全的隐患不容小觑。在这项工作中，我们提出了一种判断激励模式（JEM），通过双通道人机交互在驾驶行为中添加次要认知任务，以延迟驾驶疲劳的发生。我们使用多重分形去趋势波动分析（MF-DFA）来研究受试者脑电图的动态特性，并通过赫斯特指数值和多重分形谱宽度值分析JEM对疲劳延缓的影响。结果表明，两种驾驶模式（正常驾驶模式和JEM）的多重分形特性存在显著差异。我们提出的JEM能够有效延迟驾驶疲劳的发生，在未来实际应用中具有良好前景。
