Driver Distraction

Driver distraction, a leading cause of traffic accidents, is the focus of intense research aimed at developing accurate and real-time detection systems. Current efforts utilize various machine learning approaches, including convolutional neural networks (CNNs), vision transformers (ViTs), and spiking neural networks (SNNs), often incorporating multi-modal data (e.g., video, pose estimation) and leveraging techniques like self-supervised learning and knowledge distillation to improve accuracy and efficiency. These advancements hold significant potential for enhancing road safety through the development of advanced driver-assistance systems and informing the design of safer in-vehicle interfaces.