  Inspired by human vision, we propose a new periphery-fovea multi-resolution
driving model that predicts vehicle speed from dash camera videos. The
peripheral vision module of the model processes the full video frames in low
resolution. Its foveal vision module selects sub-regions and uses
high-resolution input from those regions to improve its driving performance. We
train the fovea selection module with supervision from driver gaze. We show
that adding high-resolution input from predicted human driver gaze locations
significantly improves the driving accuracy of the model. Our periphery-fovea
multi-resolution model outperforms a uni-resolution periphery-only model that
has the same amount of floating-point operations. More importantly, we
demonstrate that our driving model achieves a significantly higher performance
gain in pedestrian-involved critical situations than in other non-critical
situations.
