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A 20% rise in car crashes in 2021 compared to 2020 has been observed as aresult of increased distraction and drowsiness. Drowsy and distracted drivingare the cause of 45% of all car crashes. As a means to decrease drowsy anddistracted driving, detection methods using computer vision can be designed tobe low-cost, accurate, and minimally invasive. This work investigated the useof the vision transformer to outperform state-of-the-art accuracy from 3D-CNNs.Two separate transformers were trained for drowsiness and distractedness. Thedrowsy video transformer model was trained on the National Tsing-Hua UniversityDrowsy Driving Dataset (NTHU-DDD) with a Video Swin Transformer model for 10epochs on two classes -- drowsy and non-drowsy simulated over 10.5 hours. Thedistracted video transformer was trained on the Driver Monitoring Dataset (DMD)with Video Swin Transformer for 50 epochs over 9 distraction-related classes.The accuracy of the drowsiness model reached 44% and a high loss value on thetest set, indicating overfitting and poor model performance. Overfittingindicates limited training data and applied model architecture lackedquantifiable parameters to learn. The distracted model outperformedstate-of-the-art models on DMD reaching 97.5%, indicating that with sufficientdata and a strong architecture, transformers are suitable for unfit drivingdetection. Future research should use newer and stronger models such asTokenLearner to achieve higher accuracy and efficiency, merge existing datasetsto expand to detecting drunk driving and road rage to create a comprehensivesolution to prevent traffic crashes, and deploying a functioning prototype torevolutionize the automotive safety industry.

Applying Spatiotemporal Attention to Identify Distracted and Drowsy Driving with Vision Transformers