Detection of the Driver’s Mental Workload Level in Smart and Autonomous Systems Using Physiological Signals

With the continuous advancement of automation technology, autonomous driving assistance systems are gradually sharing the tasks during driving, but the driver still assumes the main driving tasks. In addition to driving activities, the advent of numerous new functions will have an indirect impact on the driver’s mental effort. However, determining the driver’s mental effort remains a difficult issue. In this paper, a method is proposed to assess the mental workload of drivers, combining real driver’s physiological data with the speed of his/her vehicle. The correlation coefficient and significance level are obtained by analyzing the correlation between physiological data and road types. The relevant data is then preprocessed to determine the characteristic index, with the mental workload as the input index. The driver’s mental workload is classified and the mental workload prediction model is constructed on the basis of the combination of the Fuzzy Pattern Recognition Algorithm and Genetic Algorithm. At the same time, the suggested approach is compared to the J48 Classification Algorithm and the Simulated Annealing Optimization Algorithm. The results demonstrate that the proposed method in this paper’s effectiveness for identifying the driver’s mental workload level is evidently better than other algorithms, which provides new theoretical support for assessing the L3+ driver’s mental workload level under the background of the safety of the intended functionality when they take over the control of the drive.