Fatigue Measurement of Task: Based on Multiple EyeTracking Parameters and Task Performance. Human error can be reduced by monitoring fatigue during work and taking measures so that accidents can be effectively prevent. In this paper, participants accumulated fatigue during the process of using the experimental program integrating the open source SDK, and their task performance and PERLCLOS were collected and calculated by the experimental program. Finally, a neural network model was established to describe the relationship between various parameters and fatigue. Through experiments, it is found that PERCLOS and task performance can well reflect fatigue state in tasks of searching. This model can detect the fatigue degree of different people in specific application scenarios.

Driver fatigue transition prediction in highly automated driving using physiological features Abstract   One of the main causes of traffic accidents is driver fatigue due to monotonous driving, sleep deprivation, boredom, or a combination of these. Thus, fatigue detection systems have been proposed to alert drivers. However, how early driver fatigue can be detected often determines the effectiveness of the system. Traditional approaches aim to detect driver fatigue in real time, which can be too late in many critical situations, such as the takeover transition period in highly automated driving. Therefore, in this research, we aim to predict the driveru0027s transition from nonfatigue to fatigue in highly automated driving using physiological features. First, we capitalized on PERCLOS (i.e., PERcent of time the eyelids CLOSure) as the ground truth of driver fatigue. Next, we selected the most important physiological features to predict driver fatigue proactively. Finally, using these critical physiological features, we built prediction models that were able to predict the fatigue transition at least 13.8xc2xa0s ahead of time using a technique called nonlinear autoregressive exogenous network. The accuracy of fatigue transition prediction was promising for highly automated driving (F1 measurexc2xa0xc2xa097.4% and 99.1% for two types of models), which demonstrated the potential of the proposed method.