  Human drivers have distinct driving techniques, knowledge, and sentiments due
to unique driving traits. Driver drowsiness has been a serious issue
endangering road safety; therefore, it is essential to design an effective
drowsiness detection algorithm to bypass road accidents. Miscellaneous research
efforts have been approached the problem of detecting anomalous human driver
behaviour to examine the frontal face of the driver and automobile dynamics via
computer vision techniques. Still, the conventional methods cannot capture
complicated driver behaviour features. However, with the origin of deep
learning architectures, a substantial amount of research has also been executed
to analyze and recognize driver's drowsiness using neural network algorithms.
This paper introduces a novel framework based on vision transformers and YoloV5
architectures for driver drowsiness recognition. A custom YoloV5 pre-trained
architecture is proposed for face extraction with the aim of extracting Region
of Interest (ROI). Owing to the limitations of previous architectures, this
paper introduces vision transformers for binary image classification which is
trained and validated on a public dataset UTA-RLDD. The model had achieved
96.2\% and 97.4\% as it's training and validation accuracies respectively. For
the further evaluation, proposed framework is tested on a custom dataset of 39
participants in various light circumstances and achieved 95.5\% accuracy. The
conducted experimentations revealed the significant potential of our framework
for practical applications in smart transportation systems.
