  Ensuring traffic safety and mitigating accidents in modern driving is of
paramount importance, and computer vision technologies have the potential to
significantly contribute to this goal. This paper presents a multi-modal Vision
Transformer for Driver Distraction Detection (termed ViT-DD), which
incorporates inductive information from training signals related to both
distraction detection and driver emotion recognition. Additionally, a
self-learning algorithm is developed, allowing for the seamless integration of
driver data without emotion labels into the multi-task training process of
ViT-DD. Experimental results reveal that the proposed ViT-DD surpasses existing
state-of-the-art methods for driver distraction detection by 6.5% and 0.9% on
the SFDDD and AUCDD datasets, respectively.
