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.

翻译：从驾驶视角预测驾驶员注意力的问题因其对自动驾驶和辅助驾驶系统的显著意义而日益受到研究关注。驾驶经验对于安全驾驶至关重要——经验丰富的驾驶员能够基于驾驶经验在危险变得显著之前轻松预判潜在风险，并快速关注相应区域。然而，非客观的驾驶经验难以建模，现有方法中缺乏模拟驾驶员经验积累过程的机制，当前方法通常遵循显著性预测方法的技术路线来预测驾驶员注意力。本文提出一种反馈循环网络（FBLNet），尝试对驾驶经验积累过程进行建模。通过反复迭代，FBLNet生成携带丰富历史累积与长期时序信息的增量知识。该增量知识类似于人类驾驶经验。在增量知识引导下，模型融合从输入图像中提取的CNN特征与Transformer特征来预测驾驶员注意力。我们的模型相较现有方法展现出显著优势，在两个驾驶员注意力基准数据集上均实现了卓越的性能提升。