A modeled study of driver visual attention driven by driving tasks

Abstract

Visual attention is an indispensable component of driving, enabling drivers to swiftly identify critical objects within complex and dynamic traffic environments. Despite its significance, existing visual attention models predominantly focus on static or idealized driving scenarios, limiting their ability to capture attention distribution patterns in real-world, dynamic environments. Furthermore, most of these models rely heavily on data-driven approaches, extracting features exclusively from visual image data, while neglecting the profound influence of “the driver, the vehicle, and the road environment”. Consequently, these models frequently fail to effectively address the intricacies of practical driving scenarios. To bridge these gaps, this study introduces a driver visual attention prediction model that comprehensively incorporates the driving task, driver experience, and the impact of dynamic visual scenes. The proposed model leverages the advanced learning capabilities of Convolutional Neural Networks (CNN) and Vision Transformer (ViT), coupled with sequence modeling mechanisms, to effectively capture the nuanced attention allocation patterns of drivers in complex driving contexts. The model is meticulously designed to adapt to dynamically evolving driving task requirements. Experimental results demonstrate that the proposed model outperforms state-of-the-art (SOTA) visual attention prediction models across multiple benchmark evaluation metrics on the DR(eye)VE dataset, particularly excelling in dynamic driving conditions. Moreover, generalization experiments were conducted on the BDD-A and TDV datasets validate the model’s robustness and applicability across varied driving tasks and dynamic conditions.