  The problem of predicting driver attention from the driving perspective is
gaining increasing research focus due to its remarkable significance for
autonomous driving and assisted driving systems. The driving experience is
extremely important for safe driving,a skilled driver is able to effortlessly
predict oncoming danger (before it becomes salient) based on the driving
experience and quickly pay attention to the corresponding zones.However, the
nonobjective driving experience is difficult to model, so a mechanism
simulating the driver experience accumulation procedure is absent in existing
methods, and the current methods usually follow the technique line of saliency
prediction methods to predict driver attention. In this paper, we propose a
FeedBack Loop Network (FBLNet), which attempts to model the driving experience
accumulation procedure. By over-and-over iterations, FBLNet generates the
incremental knowledge that carries rich historically-accumulative and long-term
temporal information. The incremental knowledge in our model is like the
driving experience of humans. Under the guidance of the incremental knowledge,
our model fuses the CNN feature and Transformer feature that are extracted from
the input image to predict driver attention. Our model exhibits a solid
advantage over existing methods, achieving an outstanding performance
improvement on two driver attention benchmark datasets.
