Abstract: Fatigue is one of major contributing factor to traffic accidents worldwide. To save many human lives, research on detecting driver fatigue using various methods continues to be developed. Ease of implementation and low cost are key challenges in any research study. Another challenge is the ability to measure fatigue significantly and in more realistic scenarios. This study aims to measure fatigue levels using driver behavioral measurement methods with the addition of cognitive load, like real-world driver tasks. This study used embedded cameras to measure driver behavior. The parameters used were body and head posture, gaze, and changes in driver expression. While driving in a simulator, drivers are tasked with following the car in front of them while maintaining distance and speed, and applying the brake pedal when the car in front suddenly stops. By implementing a one-class support vector machine (OCSVM), it was found that, in general, although each parameter tends to vary between individuals, there are significant anomalies in facial position and rotation along with driving duration, indicating driver fatigue. Meanwhile, there was a slight anomaly in body position, although subjective measurements indicated that all participants felt a higher workload after using the driving simulator

Keywords: Fatigue driving, behavioral, one-class SVM, cognitive load