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.

本文旨在全面回顾驾驶员凝视基础知识、估计驾驶员凝视的方法以及其在现实世界驾驶场景中的应用，包括了算法、数据集、驾驶行为等方面，并讨论了现有文献中的局限性、挑战和未来的研究方向。