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Human drivers have distinct driving techniques, knowledge, and sentiments dueto unique driving traits. Driver drowsiness has been a serious issueendangering road safety; therefore, it is essential to design an effectivedrowsiness detection algorithm to bypass road accidents. Miscellaneous researchefforts have been approached the problem of detecting anomalous human driverbehaviour to examine the frontal face of the driver and automobile dynamics viacomputer vision techniques. Still, the conventional methods cannot capturecomplicated driver behaviour features. However, with the origin of deeplearning architectures, a substantial amount of research has also been executedto analyze and recognize driver's drowsiness using neural network algorithms.This paper introduces a novel framework based on vision transformers and YoloV5architectures for driver drowsiness recognition. A custom YoloV5 pre-trainedarchitecture is proposed for face extraction with the aim of extracting Regionof Interest (ROI). Owing to the limitations of previous architectures, thispaper introduces vision transformers for binary image classification which istrained and validated on a public dataset UTA-RLDD. The model had achieved96.2\% and 97.4\% as it's training and validation accuracies respectively. Forthe further evaluation, proposed framework is tested on a custom dataset of 39participants in various light circumstances and achieved 95.5\% accuracy. Theconducted experimentations revealed the significant potential of our frameworkfor practical applications in smart transportation systems.

Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework