Blockchain-Based Vision Transformer Network for Safe Driving Perception

Driver distraction is a principal cause of traffic accidents. In a study conducted by the National Highway Traffic Safety Administration, engaging in activities such as interacting with in-car menus, consuming food or beverages, or engaging in telephonic conversations while operating a vehicle can be significant sources of driver distraction. From this viewpoint, this article introduces a novel method for detection of driver distraction using multiview driver action images. The proposed method is a vision transformer (ViT)-based framework with pose estimation and action inference, namely, PoseViNet. The motivation for adding posture information is to enable the transformer to focus more on key features. As a result, the framework is more adept at identifying critical actions. The proposed framework is compared with various state-of-the-art models using the SFD3 dataset representing ten behaviors of drivers. It is found from the comparison that the PoseViNet outperforms these models. The proposed framework is also evaluated with the SynDD1 dataset representing 16 behaviors of driver. As a result, the PoseViNet achieves 97.55% validation accuracy and 90.92% testing accuracy with the challenging dataset. Furthermore, the PoseViNet is integrated with a private blockchain network to secure and efficient real-time and accurate data transmission to end-users targeting to make a transparent and traceable system.