Autonomous Trucks Fleet Management System

A comprehensive web-based fleet management system for monitoring, tracking, and managing autonomous trucks. Real-time tracking with Google Maps, intelligent scheduling, maintenance alerts, and full user management.

Add these topics to your GitHub repo: fleet-management, autonomous-vehicles, logistics, python, flask, mongodb, real-time-tracking, google-maps, web-application, transportation, iot, dashboard

Features

Core Fleet Management

• Real-time Truck Tracking - Monitor truck locations on interactive Google Maps
• Fleet Dashboard - Overview of fleet status, active trucks, and alerts at a glance
• Truck Management - Add, update, and delete trucks from the fleet
• Status Monitoring - Track truck status (Active, Maintenance, Idle, En Route)

Operations

• Route Scheduling - Schedule routes and deliveries with departure/arrival times
• Service Requests - Request and track maintenance and service for trucks
• Alert System - Real-time alerts with severity levels (high, medium, low)
• Reports & Analytics - Generate reports on fleet performance and truck status

User Management

• Secure Authentication - Session-based login with password hashing (Werkzeug)
• User Profiles - Manage user information and preferences
• Role-based Access - Login-required decorator for protected routes

Advanced Features

• Simulation Integration - Carla 0.9.14 integration for testing scenarios
• Multi-Collection Database - Organized data structure with MongoDB
• Responsive UI - Modern dashboard with CSS styling
• Real-time Updates - Live fleet status and alert notifications

Screenshots

Coming soon - Dashboard and tracking interface screenshots will be added here

Dashboard	Map Tracking	Alerts

Tech Stack

Backend

• Flask 3.0 - Lightweight Python web framework
• PyMongo - MongoDB driver for Python
• Werkzeug - Password hashing and security utilities

Database

• MongoDB 4.0+ - NoSQL database with 5 collections:
  • trucks - Fleet vehicle data
  • users - User accounts and profiles
  • schedules - Route scheduling
  • service_requests - Maintenance tracking
  • alerts - Real-time alert system

Frontend

• Jinja2 Templates - Server-side rendering with template inheritance
• HTML5/CSS3 - Responsive, modern UI
• JavaScript - Interactive features
• Google Maps API - Real-time location tracking

Simulation

• Carla 0.9.14 - Autonomous vehicle simulation platform

Architecture

autonomous_trucks/
├── app.py                    # Flask application & routes (515 lines)
├── models.py                 # Database models & business logic
├── db.py                     # MongoDB connection & collections
├── config.py                 # Configuration management
├── static/
│   └── styles.css           # UI styling (12KB)
├── templates/               # 14 Jinja2 templates
│   ├── base.html            # Base template with navigation
│   ├── index.html           # Dashboard with fleet summary
│   ├── track_map.html       # Google Maps tracking interface
│   ├── track_trucks.html    # List view of truck locations
│   ├── schedule.html        # Route scheduling
│   ├── request_service.html # Maintenance requests
│   ├── alerts.html          # Alert management
│   ├── reports.html         # Analytics & reports
│   ├── simulation.html      # Simulation controls
│   ├── add_truck.html       # Add new truck form
│   ├── update.html          # Update truck status
│   ├── login.html           # User login
│   ├── register.html        # User registration
│   └── profile.html         # User profile management
├── requirements.txt         # Python dependencies
├── .env.example            # Environment variable template
└── README.md               # This file

Installation

Prerequisites

• Python 3.7+ - Programming language
• MongoDB 4.0+ - Database server
• Google Maps API Key - For location tracking
• Carla 0.9.14 (Optional) - For simulation features

Setup Instructions

1. Clone the repository

   git clone https://github.com/varun-dudipala/autonomous-trucks.git
   cd autonomous-trucks

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up MongoDB

   # Install MongoDB (if not already installed)
   # macOS: brew install mongodb-community
   # Ubuntu: sudo apt install mongodb
   
   # Start MongoDB service
   # macOS: brew services start mongodb-community
   # Ubuntu: sudo systemctl start mongodb
   
   # Verify MongoDB is running on localhost:27017
   mongo --eval "db.version()"

5. Configure environment variables

   cp .env.example .env
   # Edit .env and add your credentials:
   # - GOOGLE_MAPS_API_KEY
   # - SECRET_KEY
   # - MONGODB_URI (optional, defaults to localhost)

6. Get Google Maps API Key

   • Go to Google Cloud Console
   • Create a new project or select existing
   • Enable Maps JavaScript API
   • Create API key
   • Add key to .env file

7. Run the application

   python app.py

8. Access the application

   • Open browser to http://localhost:5000
   • Register a new account
   • Start managing your fleet!

Usage

Getting Started

1. Register/Login: Create an account at /register or login at /login
2. Add Trucks: Navigate to "Add Truck" and enter truck details
3. View Dashboard: Main dashboard shows fleet overview with statistics
4. Track Trucks: Use map view or list view to monitor locations

Route Management

• Schedule Route: Go to Schedule page, select truck, enter destination and times
• View Schedules: See all scheduled routes with departure/arrival times
• Update Status: Mark routes as completed or in-progress

Maintenance

• Request Service: Submit service requests with type (maintenance, repair, inspection)
• Track Requests: View all service requests and their status (Pending, In Progress, Completed)
• View Alerts: Monitor maintenance alerts and critical notifications

Analytics

• Reports: Generate fleet performance reports
• Alert History: View past alerts and their severity
• Simulation: Run test scenarios to validate fleet behavior

API Routes

Authentication

• GET/POST /login - User login
• GET/POST /register - User registration
• GET /logout - User logout

Fleet Management

• GET / - Dashboard (requires login)
• GET/POST /add - Add new truck
• POST /update/<truck_id> - Update truck status
• POST /delete/<truck_id> - Delete truck

Tracking

• GET /track_trucks - List view of trucks
• GET /track_map - Map view with Google Maps

Operations

• GET/POST /schedule - Manage schedules
• GET/POST /request_service - Service requests
• GET /alerts - View and manage alerts

User

• GET/POST /profile - User profile management

Analytics

• GET /reports - Fleet reports
• GET /simulation - Simulation interface

Configuration

Edit config.py or use environment variables:

# Database
MONGODB_URI = "mongodb://localhost:27017/"
DATABASE_NAME = "autonomous_trucks"

# Google Maps
GOOGLE_MAPS_API_KEY = "your_api_key_here"

# Flask
SECRET_KEY = "your_secret_key_here"
DEBUG = False
HOST = "0.0.0.0"
PORT = 5000

# Simulation
CARLA_HOST = "localhost"
CARLA_PORT = 2000

Database Schema

Trucks Collection

{
  "truck_id": "string",
  "location": "string",
  "speed": "number",
  "status": "Active|Maintenance|Idle|En Route"
}

Users Collection

{
  "username": "string",
  "password": "hashed_string",
  "full_name": "string",
  "email": "string",
  "phone": "string"
}

Schedules Collection

{
  "truck_id": "string",
  "destination": "string",
  "departure_time": "datetime",
  "arrival_time": "datetime",
  "status": "Scheduled|In Progress|Completed"
}

Service Requests Collection

{
  "truck_id": "string",
  "service_type": "Maintenance|Repair|Inspection",
  "description": "string",
  "requested_date": "datetime",
  "requested_by": "string",
  "status": "Pending|In Progress|Completed"
}

Alerts Collection

{
  "alert_type": "maintenance|safety|operational",
  "truck_id": "string",
  "message": "string",
  "severity": "high|medium|low",
  "timestamp": "datetime",
  "read": "boolean",
  "acknowledged": "boolean"
}

Security Features

• Password Hashing: Werkzeug's generate_password_hash and check_password_hash
• Session Management: Flask sessions with secure secret key
• Protected Routes: @login_required decorator for authenticated access
• Environment Variables: Sensitive data stored in .env file
• Input Validation: Form validation on registration and data entry

Performance Considerations

• MongoDB Indexing: Consider adding indexes on frequently queried fields
• Connection Pooling: MongoDB client handles connection pooling automatically
• Caching: Can add Redis for session caching in production
• Load Balancing: Use Gunicorn + Nginx for production deployment

What I Learned

• Full-stack development with Flask and MongoDB
• Real-time tracking systems with Google Maps API
• Database design for complex relationships (trucks, schedules, alerts)
• User authentication and session management
• RESTful API design principles
• Template inheritance with Jinja2
• IoT integration patterns for vehicle tracking
• Alert systems with severity levels and notifications

Future Enhancements

• WebSocket integration for real-time updates
• Mobile app for drivers (React Native)
• AI-powered route optimization
• Predictive maintenance using ML
• Integration with telematics devices
• Multi-tenant support for fleet operators
• Advanced analytics dashboard with charts
• Email/SMS notifications for critical alerts
• Export reports to PDF/CSV
• API for third-party integrations

Deployment

Production Deployment

Using Gunicorn + Nginx:

# Install Gunicorn
pip install gunicorn

# Run with Gunicorn
gunicorn -w 4 -b 0.0.0.0:5000 app:app

Docker Deployment:

FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:5000", "app:app"]

Cloud Platforms:

• Heroku: Use Procfile with Gunicorn
• AWS EC2: Deploy with Nginx reverse proxy
• Google Cloud Run: Containerized deployment
• Railway: Simple git-based deployment

See DEPLOYMENT.md for detailed deployment instructions.

Contributing

Contributions are welcome! Please read CONTRIBUTING.md for guidelines on how to contribute to this project.

License

MIT License - See LICENSE file for details.

Acknowledgments

• Google Maps API for location tracking
• Carla Simulator for autonomous vehicle testing
• Flask framework and MongoDB for robust backend

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Built with Flask, MongoDB, and Google Maps API