FSDAM
Explainable driver attention modeling with vision-language learning
FSDAM investigates explainable driver attention modeling by combining visual attention prediction with language-based reasoning. The project aims to better understand where drivers look, why they attend to particular regions, and how attention can be interpreted in driving scenes.
This work connects driver attention analysis, multimodal learning, and human-centered AI for automated vehicles.