# Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction

arXiv: https://arxiv.org/abs/1802.00066 · HTML: https://mlsanity.com/paper/1802.00066/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2018.

## Built on by (most cited first, 15 of 15)

- [Important Object Identification with Semi-Supervised Learning for Autonomous Driving](https://mlsanity.com/paper/2203.02634/) (2022)
- [A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding](https://mlsanity.com/paper/2307.01470/) (2023)
- [Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation](https://mlsanity.com/paper/2107.12932/) (2021)
- [Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation](https://mlsanity.com/paper/2311.16091/) (2023)
- [MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perception](https://mlsanity.com/paper/2504.02264/) (2025)
- [M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving](https://mlsanity.com/paper/2403.12552/) (2024)
- [FR-Net:A Light-weight FFT Residual Net For Gaze Estimation](https://mlsanity.com/paper/2305.11875/) (2023)
- [UV-M3TL: A Unified and Versatile Multimodal Multi-Task Learning Framework for Assistive Driving Perception](https://mlsanity.com/paper/2602.01594/) (2026)
- [Driver Attention Tracking and Analysis](https://mlsanity.com/paper/2404.07122/) (2024)
- [EraW-Net: Enhance-Refine-Align W-Net for Scene-Associated Driver Attention Estimation](https://mlsanity.com/paper/2408.08570/) (2024)
- [Vision-based Analysis of Driver Activity and Driving Performance Under the Influence of Alcohol](https://mlsanity.com/paper/2309.08021/) (2023)
- [Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region Estimation Methods in a Driving Simulator](https://mlsanity.com/paper/2402.05248/) (2024)
- [STDA: Spatio-Temporal Dual-Encoder Network Incorporating Driver Attention to Predict Driver Behaviors Under Safety-Critical Scenarios](https://mlsanity.com/paper/2408.01774/) (2024)
- [OmniGF: A Dual-Branch Vision-Language Framework for Unified Gaze Following](https://mlsanity.com/paper/2605.26399/) (2026)
- [TransGaze-Object: Transformer Based Driver Gaze Object Prediction Framework in Real Driving](https://mlsanity.com/paper/2609.10139/) (2026)

## Abstract (arXiv)

The study and modeling of driver's gaze dynamics is important because, if and how the driver is monitoring the driving environment is vital for driver assistance in manual mode, for take-over requests in highly automated mode and for semantic perception of the surround in fully autonomous mode. We developed a machine vision based framework to classify driver's gaze into context rich zones of interest and model driver's gaze behavior by representing gaze dynamics over a time period using gaze accumulation, glance duration and glance frequencies. As a use case, we explore the driver's gaze dynamic patterns during maneuvers executed in freeway driving, namely, left lane change maneuver, right lane change maneuver and lane keeping. It is shown that condensing gaze dynamics into durations and frequencies leads to recurring patterns based on driver activities. Furthermore, modeling these patterns show predictive powers in maneuver detection up to a few hundred milliseconds a priori.