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.

翻译：驾驶员注视在多种基于注视的应用中扮演重要角色，例如驾驶员注意力检测、视觉分心检测、注视行为理解以及驾驶辅助系统的构建。本研究的主要目标是对驾驶员注视的基本原理、注视估计方法及其在真实驾驶场景中的应用进行全面总结。我们首先探讨与驾驶员注视相关的基础知识，包括基于头戴式设备和远程设置的注视估计方法，以及每种数据采集方法所使用的术语。接着，我们列举了现有的基准驾驶员注视数据集，重点说明数据集的采集方法和所使用的设备。随后，我们讨论了用于驾驶员注视估计的算法，主要包括基于传统机器学习和深度学习的技术。利用估计出的驾驶员注视，进一步分析在通过交叉路口、匝道入口、匝道出口、变道时的注视行为，并评估路边广告结构的影响。最后，我们讨论了现有文献中的局限性、挑战以及驾驶员注视估计和基于注视的应用的未来发展方向。