https://github.com/jeon-jinhyeok/embeded-driver-drowsiness

Embeded System Project - Driver Drowsiness Detection
https://github.com/jeon-jinhyeok/embeded-driver-drowsiness

driver-drowsiness-detection embeded esp-32 stm32

Last synced: 11 days ago
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Embeded System Project - Driver Drowsiness Detection

• Host: GitHub
• URL: https://github.com/jeon-jinhyeok/embeded-driver-drowsiness
• Owner: Jeon-Jinhyeok
• Created: 2024-12-04T11:41:05.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-02-14T14:05:25.000Z (3 months ago)
• Last Synced: 2025-05-07T21:48:42.796Z (12 days ago)
• Topics: driver-drowsiness-detection, embeded, esp-32, stm32
• Language: C
• Homepage:
• Size: 31.3 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 🚗 Driver Drowsiness Detection Embeded System

![License](https://img.shields.io/badge/license-MIT-blue.svg)

![Platform](https://img.shields.io/badge/platform-STM32-0A7CCF.svg)

![Arduino](https://img.shields.io/badge/Compatible-Arduino-00979D.svg)

![Android](https://img.shields.io/badge/Compatible-Android-3DDC84.svg)

![Framework](https://img.shields.io/badge/framework-TensorFlow_Lite-FF6F00.svg)

![Language](https://img.shields.io/badge/language-C%2B%2B-blue.svg)

 

## 📖 Table of Contents

1. [Project Overview](#-project-overview)

2. [Technologies & Hardware](#-technologies--hardware)

3. [System Configuration](#-system-configuration)

4. [Expected Benefits](#-expected-benefits)

5. [License](#-license)

## 📌 Project Overview

The Driver Drowsiness Detection System is an embedded system designed to analyze the driver’s face in real-time and determine drowsiness.

By implementing this system, we aim to prevent accidents caused by drowsy driving and create a safer driving environment.

## Features

## 🛠 Technologies & Hardware

- **Embedded Board**: STM32F10x

- **Camera Module**: ESP32-CAM

- **Communication Methods**:

  - ESP32-CAM ↔ STM32F Board: USART1

  - STM32F Board ↔ Bluetooth Module: USART2

  - Bluetooth Module ↔ Android: Bluetooth Communication 

- **Timer Usage**:

  - TIM2: PIR sensor input processing

  - TIM3: Vibration motor control (PWM output)

- **Drowsiness Detection Model**:

  - Dataset: NTHU Drowsy Driver Detection Dataset

  - Model Architecture: CNN

  - Framework: TensorFlow Lite (converted to TFLite for embedded implementation)

 

## ⚙ System Configuration

1. **ESP32-CAM**: Captures the driver’s face in real-time and processes the image using the TFLite model.

2. **STM32F Board**: 

   - Manages communication between ESP32-CAM, Bluetooth module, and Android Smartphone.

   - Handles PIR sensor input for detecting driver presence.

   - Controls the vibration motor using PWM output based on received drowsiness alerts.

   - //추가..

3. **Bluetooth Module**:

   - Connects to a smartphone app to display alerts and warnings.

   - Can be integrated with external alert devices (e.g., speakers, vibration motors).

4. **Android Smartphone**:

   - Receives Bluetooth alerts from STM32F Board.

   - **Automatically plays music** when a drowsiness alert is received.

5. **Vibration Motor**: Provides a physical alert when drowsiness is detected.

6. **PIR Sensor**: Detects the presence of the driver.

7. **Light Sensor**: Adjusts drowsiness detection sensitivity dynamically under varying lighting conditions.

## 🚀 Expected Benefits

- **Enhanced Driver Safety**: Prevents accidents caused by drowsy driving.

- **Integrated Alert System**: Utilizes visual, auditory, and haptic feedback for effective warnings.

- **Automatic Music Playback**: Helps keep the driver awake by playing music upon drowsiness detection.

- **Optimized Embedded System**: Uses a lightweight model for real-time analysis.

## 🏗 Future Improvements

- Enhance accuracy by training the model with additional datasets.

- Improve smartphone app with additional UI features.

- Incorporate vehicle interior environment data (temperature, lighting, etc.).

## 📜 License

MIT License