# Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles

arXiv: https://arxiv.org/abs/2507.04139 · HTML: https://mlsanity.com/paper/2507.04139/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 5 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text](https://mlsanity.com/paper/2104.11178/) (2021)
- [DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis](https://mlsanity.com/paper/2008.12085/) (2020)
- [3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation](https://mlsanity.com/paper/2312.00311/) (2023)
- [Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness](https://mlsanity.com/paper/1811.06047/) (2018)
- [DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data](https://mlsanity.com/paper/2012.15441/) (2020)
- [Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework](https://mlsanity.com/paper/1910.09722/) (2019)
- [Evaluating Driver Readiness in Conditionally Automated Vehicles from Eye-Tracking Data and Head Pose](https://mlsanity.com/paper/2401.11284/) (2024)

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

- [C-DIRA: Computationally Efficient Dynamic ROI Routing and Domain-Invariant Adversarial Learning for Lightweight Driver Behavior Recognition](https://mlsanity.com/paper/2512.08647/) (2025)
- [Multi-modal Video Representation Alignment for Robust Self-supervised Driver Distraction Detection](https://mlsanity.com/paper/2606.02352/) (2026)

## Abstract (arXiv)

Ensuring safe transition of control in automated vehicles requires an accurate and timely assessment of driver readiness. This paper introduces Driver-Net, a novel deep learning framework that fuses multi-camera inputs to estimate driver take-over readiness. Unlike conventional vision-based driver monitoring systems that focus on head pose or eye gaze, Driver-Net captures synchronised visual cues from the driver's head, hands, and body posture through a triple-camera setup. The model integrates spatio-temporal data using a dual-path architecture, comprising a Context Block and a Feature Block, followed by a cross-modal fusion strategy to enhance prediction accuracy. Evaluated on a diverse dataset collected from the University of Leeds Driving Simulator, the proposed method achieves an accuracy of up to 95.8% in driver readiness classification. This performance significantly enhances existing approaches and highlights the importance of multimodal and multi-view fusion. As a real-time, non-intrusive solution, Driver-Net contributes meaningfully to the development of safer and more reliable automated vehicles and aligns with new regulatory mandates and upcoming safety standards.