考虑噪声标签影响的驾驶员精神负荷状态评价

Evaluation of Driver’s Mental Load State Considering the Influence of Noisy Labels

现有驾驶员精神负荷评价研究多以驾驶场景中有无次任务来给定驾驶员的精神负荷分类标签,但驾驶员在正常驾驶情景下也可能由于陷入自我思维而导致精神负荷的增加;此外,由于个体差异,同一驾驶次任务对不同驾驶员精神负荷的影响也不尽相同。因此,由传统方法所制作的数据集可能存在噪声标签,从而影响精神负荷评价模型的训练效果。针对此类问题,本文中采用置信学习的方法对驾驶员的精神负荷分类标签进行检测和滤除,使用处理过的标签,以脑电、心电和皮电信号特征作为模型输入,基于支持向量机、随机森林、K近邻、决策树、逻辑回归和多层感知机等多种算法构建驾驶员精神负荷模型,对比分析噪声标签处理对提高各类模型性能的效果。结果表明:使用置信学习进行噪声标签处理后,所构建的多种驾驶员精神负荷模型的性能均得到了明显的改善,其中,支持向量机模型的性能提升的效果最佳。

Most existing studies on driver’s mental load evaluation give the driver’s mental load classification label with the presence or absence of sub-tasks in driving scenes,but drivers may sometime fall into self-thinking,leading to the increase in their mental load in normal driving scenes. In addition,even the same driving subtasks may not have the same effects on the mental load of different drivers due to the discrepancy of individuals. As a result,there may exist some noisy labels in the data set collected by traditional methods,hence affecting the training results of the mental load evaluation models of drivers. In view of these problems,the method of confidence learning is adopted to process(detect and filter)the mental load classification labels of drivers in this paper. By using the processed labels,with electroencephalogram,electrocardiogram and skin electricity signal features as model inputs,the driver’s mental load models based on algorithms of support vector machine,random forest,K-near neighbor,decision tree,logic regression,and multi-layer perceptron are constructed to comparatively analyze the effects of noisy label processing on the enhancement of the performance of different models. The results show that after the noise label processing by using confidence learning,the performances of various driver’s mental load models constructed remarkably improve,among which support vector machine model achieves the best results in performance enhancement.