Multi-Task Vision Transformer for Semi-Supervised Driver Distraction Detection

Yunsheng Ma, Ziran Wang

Driver distraction detection is an important computer vision problem that can play a crucial role in enhancing traffic safety and reducing traffic accidents. In this paper, a Vision Transformer (ViT) based approach for driver distraction detection is proposed. Specifically, a multi-modal Vision Transformer (ViT-DD) is developed, which exploits inductive information contained in signals of distraction detection as well as driver emotion recognition. Further, a semi-surprised learning algorithm is designed to include driver data without emotion labels into the supervised multi-task training of ViT-DD. Extensive experiments conducted on the SFDDD and AUCDD datasets demonstrate that the proposed ViT-DD outperforms the state-of-the-art approaches for driver distraction detection by 6.5% and 0.9%, respectively. Our source code is released at https://github.com/PurdueDigitalTwin/ViT-DD.

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