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.
翻译:从驾驶角度预测驱动器注意力的问题正在获得越来越多的研究重点,因为其对自主驾驶和辅助驾驶系统的显著意义。驾驶经验对于驾驶员注意预测极为重要,熟练驾驶员能够根据驾驶经验不遗余力地预测危险(在驾驶经验显眼之前),并迅速关注相应的区域。然而,不客观驾驶经验难以模型,因此在现有方法中不存在一种模拟驾驶员经验积累程序的机制,现有方法通常遵循突出预测驱动器注意的预测方法的技术线。在本文件中,我们提议建立一个FefBack Loop网络(FBLNet),以尝试模拟驾驶员累积程序。FBLNet通过反复反复重复,生成了具有丰富历史累积性长期时间信息的递增知识。我们模型的累积知识是人类的驱动经验。在渐进知识的指导下,我们的模型结合了从输入图像中提取的CNN特征和变异器特征,以预测驱动器注意。我们的模型展示了现有方法的坚实优势,在三种公共数据集上实现了平均10.3%的改进。