# Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework

arXiv: https://arxiv.org/abs/1910.09722 · HTML: https://mlsanity.com/paper/1910.09722/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 5 of 5)

- [Behavioral Research and Practical Models of Drivers' Attention](https://mlsanity.com/paper/2104.05677/) (2021)
- [Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework](https://mlsanity.com/paper/2209.01401/) (2022)
- [Driver-Net: Multi-Camera Fusion for Assessing Driver Take-Over Readiness in Automated Vehicles](https://mlsanity.com/paper/2507.04139/) (2025)
- [Real-Time In-Cabin Driver Behavior Recognition on Low-Cost Edge Hardware](https://mlsanity.com/paper/2512.22298/) (2025)
- [CFRNet: Cycle-Consistent Fixed-Point Training for Real-Time Blind Face Restoration on Consumer Embedded NPUs](https://mlsanity.com/paper/2606.06850/) (2026)

## Abstract (arXiv)

We propose a condition-adaptive representation learning framework for the driver drowsiness detection based on 3D-deep convolutional neural network. The proposed framework consists of four models: spatio-temporal representation learning, scene condition understanding, feature fusion, and drowsiness detection. The spatio-temporal representation learning extracts features that can describe motions and appearances in video simultaneously. The scene condition understanding classifies the scene conditions related to various conditions about the drivers and driving situations such as statuses of wearing glasses, illumination condition of driving, and motion of facial elements such as head, eye, and mouth. The feature fusion generates a condition-adaptive representation using two features extracted from above models. The detection model recognizes drivers drowsiness status using the condition-adaptive representation. The condition-adaptive representation learning framework can extract more discriminative features focusing on each scene condition than the general representation so that the drowsiness detection method can provide more accurate results for the various driving situations. The proposed framework is evaluated with the NTHU Drowsy Driver Detection video dataset. The experimental results show that our framework outperforms the existing drowsiness detection methods based on visual analysis.