Inferring driver workload has started to draw greater attention with the emerging automotive technology of higher autonomy. In this paper, we revisited the popular assumption of fixed workload levels determined by the driving environment, and propose a framework to generate a Personalized Driver Workload Profile (PDWP) that incorporates individual differences. A rich set of physiological and operational data from a real-traffic Electric Vehicle (EV) driving experiment was utilized. Physiological features were generated and selected from forty drivers’ electroencephalogram (EEG) and electrocardiogram (ECG) signals using multiple signal processing and machine learning techniques. A PDWP is defined as a random variable with three possible workload levels, and conditional distributions of the PDWP of the rest period and four driving environments were generated using fuzzy c-means clustering. The results revealed there exists little resemblance among the PDWPs of individual drivers, even in an identical driving environment. Moreover, some drivers exhibited strong evidence of EV range stress, but such phenomena were not universal. Our study is the first attempt to incorporate individual differences in estimating driving workload based on the direct cognitive responses using physiological data collected in a real-traffic experiment.

随着新兴的具有更高自治性的汽车技术，推断驾驶员的工作量已开始引起更多关注。在本文中，我们重新审视了由驾驶环境决定的固定工作量水平的流行假设，并提出了一个框架来生成包含个性差异的个性化驾驶员工作量档案（PDWP）。利用了来自真实交通电动汽车（EV）驾驶实验的大量生理和操作数据。使用多种信号处理和机器学习技术，从40位驾驶员的脑电图（EEG）和心电图（ECG）信号中生成并选择了生理特征。PDWP被定义为具有三个可能工作量级别的随机变量，并且使用模糊c均值聚类生成了休息时间和四个驾驶环境的PDWP的条件分布。结果表明，即使在相同的驾驶环境中，单个驾驶员的PDWP之间也几乎没有相似之处。此外，一些驾驶员表现出强烈的EV范围压力证据，但是这种现象并不普遍。