考虑个体差异性疲劳驾驶检测方法研究

研究个体差异性对疲劳驾驶检测的影响.基于驾驶模拟实验，提取了20名驾驶人不同驾驶状态下的眼动特征参数.基于支持向量机，搭建了通用检测模型，分析了不同驾驶人之间检测结果的差异性.运用配对样本t检验和方差分析分别量化了驾驶人疲劳和个体差异性对眼动特征影响的显著性水平，以及综合影响的显著性水平.搭建了个体检测模型，并进行了实验验证.结果表明，眼动特征受疲劳驾驶和驾驶人个体差异性的显著性影响，同时，个体差异性会削弱由疲劳驾驶导致的差异.在构建疲劳驾驶检测模型时应充分考虑驾驶人个体差异性.

Research on Driver Fatigue Detection Method Based on Individual Differences

The effects of individual differences on driver fatigue detection were researched. Eye movements data of 20 drivers were extracted based on the driving simulator experiment. The general detection model was developed based on support vector machine and the differences of detection results were analyzed. The significant level of influence on eye movements caused by driver fatigue and individual differences, as well as their comprehensive influence was analyzed using paired sample t test and ANOVA. Finally, individual models were developed and the conclusion was verified. The results indicate that eye movements are affected by individual differences and driver fatigue significantly, the effects of individual differences may bury differences caused by driving fatigue. Individual differences are important issues in building driver fatigue detection model.