Inspired by Norbert Wiener: FeedBack Loop Network Learning Incremental Knowledge for Driver Attention Prediction and Beyond

The problem of predicting driver attention from the driving perspective is gaining the increasing research focuses due to its remarkable significance for autonomous driving and assisted driving systems. Driving experience is extremely important for driver attention prediction, a skilled driver is able to effortlessly predict oncoming danger (before it becomes salient) based on driving experience and quickly pay attention on the corresponding zones. However, the nonobjective driving experience is difficult to model, so a mechanism simulating driver experience accumulation procedure is absent in existing methods, and the existing 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 long-term temporal information. The incremental knowledge to our model is like the driving experience to 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 solid advantage over existing methods, achieving an average 10.3% performance improvement on three public datasets.