Advanced AI Safety Technology

How Driver Safety AI Works

Learn about the technology, algorithms, and safety measures behind our drowsiness detection system

Technologies Used

Built with modern web technologies and AI frameworks

MediaPipe Face Mesh

Google's state-of-the-art ML solution for detecting 468 facial landmarks in real-time

Object Detection

CoCo-SSD model integration for real-time mobile phone usage detection

Client-Side Processing

All ML computations run in your browser - no data is sent to servers

Real-Time Analysis

60 FPS processing with optimized algorithms for instant detection

Detection Methodology

Multi-factor analysis pipeline for accurate risk assessment

Eye Analysis

Monitors blink rate and closure duration

Calculates Eye Aspect Ratio (EAR) to detect prolonged eye closure (micro-sleeps).

EAR Threshold < 0.25

Duration > 2 seconds

Mouth Analysis

Detects frequent yawning patterns

Measures Mouth Aspect Ratio (MAR) to identify yawning as a sign of fatigue.

MAR Threshold > 0.6

Yawn Count Tracking

Head Pose

Tracks nodding and head position

Analyzes head orientation (pitch/yaw/roll) to detect nodding off or distraction.

Angle Deviation > 25°

Nodding Detection

Distraction

Identifies mobile phone usage

Uses object detection AI to instantly recognize mobile devices in the camera frame.

Phone Object Detection

Immediate Risk Penalty

Risk Scoring Algorithm

How we calculate and categorize drowsiness levels

Safe0-39%
Normal driving

Warning40-69%
Mild drowsiness

Critical70-100%
High risk

Safety Information

This system is designed as an assistive technology and should not be considered a replacement for responsible driving practices. Always ensure adequate rest before driving. If you feel drowsy, pull over safely and take a break. Never rely solely on automated systems for safety decisions.

Ready to test the system?

Experience the real-time detection capabilities directly in your browser.

Start Detection