Mind the Gap: Quantifying and Aligning Human-AI Visual Attention for Accident Anticipation

Quantifying and understanding human-AI alignment in high-risk tasks such as traffic accident prediction is crucial for deployment of AI systems. Existing alignment studies, however, focus mostly on the static domain and neglect the importance of attentional processing. Here, we present Attention‑DADA, a dataset of accident and non-accident traffic situations that contains detailed human prediction and frame-level eye gaze annotations. Using this benchmark, we evaluate open- and closed-source, st