Transformer Architectures for Distracted Driving Behavior Detection: A Comprehensive Review of Vision-Based Approaches

Distracted driving is a leading cause of road accidents, with visual and manual distractions being particularly prevalent. Traditional computer vision methods, particularly Convolutional Neural Networks (CNNs), have been extensively utilized for detecting driver behavior; however, they face challenges in effectively modeling long-range dependencies and complex spatiotemporal patterns. Recent advancements in Vision Transformer (ViT) demonstrate significant potential to address these limitations by leveraging global attention mechanisms and a scalable architecture. This review presents a comprehensive review of ViT-based approaches in distracted driving detection, which covers both image-based and video-based methods. It examines several architectural innovations, such as lightweight ViT variants, pose-aware attention-enhanced models, and hybrid ViT-architecture designs. The review also explores multi-modal and multi-view fusion strategies, which integrate several inputs such as RGB, infrared, depth, and physiological signals to enhance model robustness across diverse driving scenarios. In addition, the paper highlights benchmark datasets and performance comparisons used in distracted driving behavior detection. Finally, this review highlights the current challenges, including computational cost and interpretability, while also proposing directions for future research. Overall, ViT-based models present a promising foundation for developing the next generation of intelligent driver monitoring systems.