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Workshop: Machine Learning for Autonomous Driving

ViT-DD: 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. This paper proposes a novel semi-supervised method for detecting driver distractions based on Vision Transformer (ViT). Specifically, a multi-modal Vision Transformer (ViT-DD) is developed that makes use of inductive information contained in training signals of distraction detection as well as driver emotion recognition. Further, a self-learning algorithm is designed to include driver data without emotion labels into the multi-task training of ViT-DD. Extensive experiments conducted on the SFDDD and AUCDD datasets demonstrate that the proposed ViT-DD outperforms the best state-of-the-art approaches for driver distraction detection by 6.5% and 0.9%, respectively.

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