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.

Detection of driver stress in realworld driving environment using physiological signals Detection of driver stress is an important component in many ADASs (Advanced DriverAssistance Systems), and a challenging problem when it is applied to realworld driving environment. In this paper, we present a convolutional neural network (CNN) designed to detect driverxe2x80x99s stress levels with four physiological signals, i.e., heart rate, heart rate variability, breathing rate, and galvanic skin response. The proposed model is shift invariant and is capable of handling the imbalanced data set issue. The performances of the proposed models are evaluated using realworld driving data in three different types of driver stress detection tasks, i.e., the singledriver, the cohorts of drivers, and alldriver stress detection task respectively. The experimental results demonstrate that the proposed model is capable of reliably detecting the driverxe2x80x99s stress levels. More importantly we demonstrate that a model can be trained on the data collected from drivers with similar cognitive capabilities and then generalized to new drivers with similar cognitive capabilities for stress detection.