Driver Gaze

Driver gaze research focuses on accurately estimating where drivers are looking in vehicles to understand their attention and intentions, crucial for developing advanced driver-assistance systems and improving road safety. Current research emphasizes improving gaze estimation accuracy using deep learning models, particularly transformer-based architectures, and addressing challenges like low-resolution images and adverse weather conditions. This involves creating large, well-annotated datasets of in-vehicle gaze data and developing models that incorporate contextual information, such as driving tasks and environmental factors, to enhance prediction accuracy. Ultimately, this work aims to improve the reliability and effectiveness of driver monitoring and assistance technologies.