Abstract
Driver gaze plays an important role in different gaze-based applications such as driver attentiveness detection, visual distraction detection, gaze behavior understanding, and building driver assistance system. The main objective of this study is to perform a comprehensive summary of driver gaze fundamentals, methods to estimate driver gaze, and it's applications in real world driving scenarios. We first discuss the fundamentals related to driver gaze, involving head-mounted and remote setup based gaze estimation and the terminologies used for each of these data collection methods. Next, we list out the existing benchmark driver gaze datasets, highlighting the collection methodology and the equipment used for such data collection. This is followed by a discussion of the algorithms used for driver gaze estimation, which primarily involves traditional machine learning and deep learning based techniques. The estimated driver gaze is then used for understanding gaze behavior while maneuvering through intersections, on-ramps, off-ramps, lane changing, and determining the effect of roadside advertising structures. Finally, we have discussed the limitations in the existing literature, challenges, and the future scope in driver gaze estimation and gaze-based applications.
Abstract (translated)
司机的目光在各种不同的 gaze based 应用中发挥着重要作用,例如司机注意力检测、视觉干扰检测、目光行为理解以及构建司机辅助系统。本研究的主要目标是对司机的目光 fundamentals、估计司机目光的方法以及它在真实世界驾驶场景中的应用进行 comprehensive summary。我们首先讨论了与司机的目光相关的 fundamentals,包括基于头戴式和远程setup 的目光估算方法,以及这些数据收集方法所使用的术语。接下来,我们列出了现有的司机目光基准数据集,重点介绍了这些数据的收集方法和所使用的设备。这之后,我们讨论了用于司机目光估算的算法,这主要涉及传统的机器学习和深度学习 based 技术。估计的司机目光被用来理解目光行为,在穿过路口、入口、出口、换车道以及确定路边广告结构的影响时进行操纵。最后,我们讨论了现有文献中的限制、挑战以及司机目光估算和 gaze-based 应用程序的未来 scope。
URL
https://arxiv.org/abs/2307.01470
