A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding

TL;DR

This paper provides a comprehensive review of driver gaze estimation methods, datasets, algorithms, and applications in real-world driving scenarios, highlighting current challenges and future directions.

Contribution

It offers an extensive summary of driver gaze fundamentals, datasets, and algorithms, and discusses their applications and limitations in real-world driving contexts.

Findings

Reviewed existing driver gaze datasets and collection methodologies.

Analyzed traditional and deep learning algorithms for gaze estimation.

Identified challenges and future research directions in driver gaze analysis.

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.