# Evaluating Driver Readiness in Conditionally Automated Vehicles from Eye-Tracking Data and Head Pose

arXiv: https://arxiv.org/abs/2401.11284 · HTML: https://mlsanity.com/paper/2401.11284/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://mlsanity.com/paper/1805.10483/) (2018)
- [Shape Preserving Facial Landmarks with Graph Attention Networks](https://mlsanity.com/paper/2210.07233/) (2022)
- [Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness](https://mlsanity.com/paper/1811.06047/) (2018)
- [Safe Control Transitions: Machine Vision Based Observable Readiness Index and Data-Driven Takeover Time Prediction](https://mlsanity.com/paper/2301.05805/) (2023)
- [Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification](https://mlsanity.com/paper/1508.04028/) (2015)
- [Using Eye-tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving](https://mlsanity.com/paper/2103.14792/) (2021)

## Built on by (most cited first, 2 of 2)

- [Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles](https://mlsanity.com/paper/2507.04139/) (2025)
- [Multi-modal Video Representation Alignment for Robust Self-supervised Driver Distraction Detection](https://mlsanity.com/paper/2606.02352/) (2026)

## Abstract (arXiv)

As automated driving technology advances, the role of the driver to resume control of the vehicle in conditionally automated vehicles becomes increasingly critical. In the SAE Level 3 or partly automated vehicles, the driver needs to be available and ready to intervene when necessary. This makes it essential to evaluate their readiness accurately. This article presents a comprehensive analysis of driver readiness assessment by combining head pose features and eye-tracking data. The study explores the effectiveness of predictive models in evaluating driver readiness, addressing the challenges of dataset limitations and limited ground truth labels. Machine learning techniques, including LSTM architectures, are utilised to model driver readiness based on the Spatio-temporal status of the driver's head pose and eye gaze. The experiments in this article revealed that a Bidirectional LSTM architecture, combining both feature sets, achieves a mean absolute error of 0.363 on the DMD dataset, demonstrating superior performance in assessing driver readiness. The modular architecture of the proposed model also allows the integration of additional driver-specific features, such as steering wheel activity, enhancing its adaptability and real-world applicability.