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Driver mental fatigue is considered a major factor affecting driver behavior that may result in fatal accidents. Several approaches are addressed in the literature to detect fatigue behavior in a timely manner through either physiological or in-vehicle measurement methods. However, the literature lacks the implementation of hybrid approaches that combine the strength of individual approaches to develop a robust fatigue detection system. In this regard, a hybrid temporal approach is proposed in this paper to detect driver mental fatigue through the combination of driver postural configuration with vehicle longitudinal and lateral behavior on a study sample of 34 diverse participants. A novel fully adaptive symbolic aggregate approximation (faSAX) algorithm is proposed, which adaptively segments and assigns symbols to the segmented time-variant fatigue patterns according to the discrepancy in postural behavior and vehicle parameters. These multivariate symbols are then combined to prepar ....

Driver Mental Fatigue , Driver Safety , Atigue Posture Patterns , Ully Adaptive Sax , Ybrid Detection System , Emantic Learning , Ehicle Situations ,