  Discrepancies in decision-making between Autonomous Driving Systems (ADS) and
human drivers underscore the need for intuitive human gaze predictors to bridge
this gap, thereby improving user trust and experience. Existing gaze datasets,
despite their value, suffer from noise that hampers effective training.
Furthermore, current gaze prediction models exhibit inconsistency across
diverse scenarios and demand substantial computational resources, restricting
their on-board deployment in autonomous vehicles. We propose a novel adaptive
cleansing technique for purging noise from existing gaze datasets, coupled with
a robust, lightweight convolutional self-attention gaze prediction model. Our
approach not only significantly enhances model generalizability and performance
by up to 12.13% but also ensures a remarkable reduction in model complexity by
up to 98.2% compared to the state-of-the art, making in-vehicle deployment
feasible to augment ADS decision visualization and performance.
