Prioritizing Where to Attend: CoAT-Based Driving Attention Guidance With Risk-Aware Modeling

Accurately identifying priority attention regions in complex driving scenarios is critical yet challenging, due to the coupling of heterogeneous factors such as motion, structured semantics, and human experience. However, existing methods often isolate these factors without systematically modeling comprehensive reasoning based on their relations, and rely on supervision from biased gaze data or indiscriminate semantic overlays, failing to highlight truly critical regions. To address these limitations, we propose the Driving Attention Guidance Network (DAG-Net) based on a specially designed Chain-of-Attention-Thought (CoAT) strategy that hierarchically decouples factors and models cognition-guided reasoning. DAG-Net comprises three key modules: 1) Entropy-Modulated Motion Module captures continuous multi-order motion features with emphasizing abrupt events modulated by entropy-guided attention; 2) Distilled Semantic Structuring Module enhances contextual representations by cross-level harmonization for hierarchical distillation of global priors, and models local spatial-structured relationships via graph convolution; and 3) novel Cognitive Alignment Reasoning Module achieves attention consensus through stepwise reasoning, where Interactive Modulation enhances cross-modal consistency of decoupled perception and Experience-Perception Synergism emulates cognitive loops of iterative refinement between perception and experience. Furthermore, we propose the first automatic annotation strategy for generating the Driving Attention Guidance Map as supervision for risk-aware attention prioritization. It quantifies object importance via collision-cost modeling with multi-modal information integration, emphasizing safety-critical regions. Experiments show our method achieves accurate and comprehensive attention guidance, enhancing contextual risk awareness and supporting safer driver assistance and autonomous driving.