14 February 2024

Driver Safety with Computer Vision and Deep Learning: How AI Detects Distraction and Fatigue

by Ming Chu

Driver Behavior Classification Overview

Why This Matters

Driver distraction is one of the leading causes of accidents.
In those moments, seconds decide lives.

Our goal was simple: build an AI system that spots distraction or fatigue in real time so drivers can be alerted before it’s too late.

From Research to Stage

This work began as my graduate research and became the focus of my 3-Minute Thesis presentation.
In three minutes, I shared how computer vision and deep learning can detect driver distraction and drowsiness.

Published in the Journal of Big Data

The research was later published in the Journal of Big Data (Springer Open):

“Comprehensive Study of Driver Behavior Monitoring Systems Using Computer Vision and Machine Learning Techniques”

The system reached 99.1% accuracy in detecting distraction and fatigue — a strong step toward safer roads.

How It Works

We combined three AI models:

CNN (Convolutional Neural Networks) — Spot visual details like eye position and head angle.
BiLSTM (Bidirectional Long Short-Term Memory) — Track behavior changes over time in both directions.
ANN (Artificial Neural Networks) — Classify the driver’s state as alert, distracted, or drowsy.

This detects subtle signs like a quick gaze shift, drooping eyelids, or hands leaving the wheel.

The Real-World Goal

If built into vehicles, this system could:

Reduce accidents caused by human error.
Improve Advanced Driver Assistance Systems (ADAS).
Add a safety layer for semi-autonomous and autonomous driving.

Looking Ahead

The next step is using Vision Transformers (ViT) trained on large, real-world driving datasets.

This will mean:

Better accuracy in complex environments.
Improved adaptability to different weather and regions.
Stronger safety when combined with data from radar, LiDAR, and driver biometrics.

What I Learned

This project strengthened my skills in:

Designing and tuning deep learning models.
Explaining complex ideas clearly.
Building technology that serves people.

It confirmed one truth for me: technology is worth building only if it protects and serves people.

Read the Full Paper

Comprehensive Study of Driver Behavior Monitoring Systems Using Computer Vision and Machine Learning Techniques

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tags: Computer Vision - Machine Learning - Autonomous Vehicles - Research