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

arXiv: https://arxiv.org/abs/2307.01470 · HTML: https://mlsanity.com/paper/2307.01470/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 216 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark](https://mlsanity.com/paper/2104.12668/) (2021)
- [Predicting the Driver's Focus of Attention: the DR(eye)VE Project](https://mlsanity.com/paper/1705.03854/) (2017)
- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture](https://mlsanity.com/paper/1601.00740/) (2016)
- [GOO: A Dataset for Gaze Object Prediction in Retail Environments](https://mlsanity.com/paper/2105.10793/) (2021)
- [Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis](https://mlsanity.com/paper/1802.02690/) (2018)
- [A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms](https://mlsanity.com/paper/1708.01817/) (2017)
- [Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset](https://mlsanity.com/paper/2004.05973/) (2020)
- [PuRe: Robust pupil detection for real-time pervasive eye tracking](https://mlsanity.com/paper/1712.08900/) (2017)
- [Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction](https://mlsanity.com/paper/1802.00066/) (2018)
- [A High-Level Description and Performance Evaluation of Pupil Invisible](https://mlsanity.com/paper/2009.00508/) (2020)
- [A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone](https://mlsanity.com/paper/1910.07331/) (2019)
- [Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification](https://mlsanity.com/paper/1508.04028/) (2015)
- [What Can Be Predicted from Six Seconds of Driver Glances?](https://mlsanity.com/paper/1611.08754/) (2016)
- [A Brief Survey on Interactive Automotive UI](https://mlsanity.com/paper/2105.14465/) (2021)

## Built on by (most cited first, 12 of 12)

- [UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training](https://mlsanity.com/paper/2502.02307/) (2025)
- [Seeing Through Deepfakes: A Human-Inspired Framework for Multi-Face Detection](https://mlsanity.com/paper/2507.14807/) (2025)
- [myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking Dataset](https://mlsanity.com/paper/2502.12723/) (2025)
- [FSDAM: Few-Shot Driving Attention Modeling via Vision-Language Coupling](https://mlsanity.com/paper/2511.12708/) (2025)
- [A Survey on Deep Learning-based Gaze Direction Regression: Searching for the State-of-the-art](https://mlsanity.com/paper/2410.17082/) (2024)
- [Investigating Bias and Fairness in Appearance-based Gaze Estimation](https://mlsanity.com/paper/2604.10707/) (2026)
- [SGAP-Gaze: Scene Grid Attention Based Point-of-Gaze Estimation Network for Driver Gaze](https://mlsanity.com/paper/2604.19888/) (2026)
- [Multi-task Gaze Estimation Via Unidirectional Convolution](https://mlsanity.com/paper/2411.18061/) (2024)
- [DMAGaze: Gaze Estimation Based on Feature Disentanglement and Multi-Scale Attention](https://mlsanity.com/paper/2504.11160/) (2025)
- [Gaze4HRI: Zero-shot Benchmarking Gaze Estimation Neural-Networks for Human-Robot Interaction](https://mlsanity.com/paper/2605.04770/) (2026)
- [TransGaze-Object: Transformer Based Driver Gaze Object Prediction Framework in Real Driving](https://mlsanity.com/paper/2609.10139/) (2026)
- [EyeTAG: Eye Trajectory-Aware Gaze Estimation](https://mlsanity.com/paper/2610.00922/) (2026)

## Abstract (arXiv)

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.