How to Build a Smart Driveway Monitor with AI-Powered Vehicle Detection

Create a cutting-edge smart driveway monitor that uses artificial intelligence to detect and identify vehicles. This project combines hardware sensors with a user-friendly mobile app, offering homeowners real-time notifications, vehicle logging, and advanced security features for their property.

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Simple Summary

A smart driveway monitor that uses advanced vehicle detection to enhance home security and convenience, providing real-time alerts and analytics for homeowners.

Product Requirements Document (PRD)

Goals:

Develop a reliable vehicle detection system for residential driveways
Create a user-friendly mobile app for monitoring and notifications
Implement AI-based vehicle recognition for enhanced security

Target Audience:

Homeowners concerned about property security
Tech-savvy individuals interested in smart home solutions

Key Features:

Real-time vehicle detection using motion sensors and cameras
AI-powered vehicle recognition and classification
Mobile app for remote monitoring and alerts
Historical logging of vehicle activity
Integration with smart home ecosystems (e.g., Google Home, Amazon Alexa)
Customizable alert settings and zones
Night vision capabilities for 24/7 monitoring

User Requirements:

Easy installation and setup process
Intuitive mobile app interface
Reliable notifications and minimal false positives
Ability to share access with family members or trusted neighbors
Privacy controls and data security measures

User Flows

New User Setup:
- Install hardware components
- Download mobile app and create account
- Connect hardware to Wi-Fi network
- Configure detection zones and alert preferences
Receiving and Responding to Alerts:
- User receives push notification of detected vehicle
- Open app to view live feed or snapshot
- Optionally trigger alarm or contact authorities
Reviewing Historical Data:
- Open mobile app and navigate to activity log
- Filter by date range or vehicle type
- View detailed information about past detections

Technical Specifications

Hardware:

Raspberry Pi 4 (main control unit)
High-resolution camera module with night vision
Motion sensors
Weather-resistant enclosure

Software:

Python for backend and AI processing
TensorFlow for vehicle detection and classification
Flask for API development
React Native for cross-platform mobile app
Firebase for real-time database and push notifications
AWS S3 for image/video storage

AI Model:

Pre-trained object detection model (e.g., YOLO or SSD)
Fine-tuned for vehicle classification

API Endpoints

/api/auth: User authentication and management
/api/devices: Manage connected devices
/api/alerts: Retrieve and manage alert settings
/api/detections: Log and retrieve vehicle detection events
/api/live: Stream live camera feed
/api/analytics: Retrieve usage statistics and insights

Database Schema

Users:

id (primary key)
email
password_hash
name
preferences

Devices:

id (primary key)
user_id (foreign key)
name
status
last_online

Detections:

id (primary key)
device_id (foreign key)
timestamp
vehicle_type
confidence_score
image_url

Alerts:

id (primary key)
user_id (foreign key)
type
settings

File Structure

/smart-driveway-monitor
  /hardware
    - main.py
    - camera.py
    - motion_sensor.py
    - ai_processor.py
  /backend
    /api
      - auth.py
      - devices.py
      - alerts.py
      - detections.py
      - live.py
      - analytics.py
    - app.py
    - config.py
    - models.py
  /mobile-app
    /src
      /components
      /screens
      /utils
    - App.js
  /ai-model
    - train.py
    - model.h5
  - README.md
  - requirements.txt

Implementation Plan

Set up development environment and version control
Develop and test hardware components (2 weeks)
Implement backend API and database (3 weeks)
Train and integrate AI model for vehicle detection (2 weeks)
Develop mobile app UI and core functionality (3 weeks)
Integrate backend with mobile app (1 week)
Implement push notifications and real-time updates (1 week)
Conduct thorough testing and bug fixes (2 weeks)
Beta testing with a small group of users (2 weeks)
Refine based on feedback and prepare for launch (1 week)

Deployment Strategy

Set up cloud infrastructure (AWS EC2 for backend, S3 for storage)
Configure CI/CD pipeline using GitHub Actions
Deploy backend API to production environment
Submit mobile app to App Store and Google Play
Provide detailed documentation for hardware setup
Implement monitoring and logging (e.g., Sentry, ELK stack)
Set up customer support channels and knowledge base
Plan for regular updates and feature improvements

Design Rationale

The combination of Raspberry Pi and custom sensors provides a cost-effective and flexible hardware solution. Python is chosen for its strong AI and IoT capabilities, while React Native enables efficient cross-platform mobile development. The AI-powered vehicle detection offers advanced features beyond simple motion sensing, justifying the use of cloud resources for processing and storage. The modular architecture allows for easy scaling and feature additions in the future.