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🚗 AutoLane: IoT-Connected Autonomous Rover

Python Cloud Hardware

📌 Project Overview

AutoLane is a prototype for a self-driving vehicle that utilizes Computer Vision for lane keeping and Cloud Computing for remote control logic.

The system captures video from an onboard camera, processes it on a host machine to calculate the optimal steering angle, and transmits control commands via Firebase to the vehicle in real-time. It also features RFID Authentication for ignition and voltage monitoring for battery safety.

⚙️ System Architecture

1. 👁️ Vision Subsystem (Python & OpenCV)

  • Input: Streams video from an IP Camera mounted on the car.
  • Processing:
    • Converts frames to HSV and applies masking for lane color.
    • Uses Canny Edge Detection and Hough Transform to identify lane lines.
    • Calculates the steering angle based on the lane curvature.
  • Output: Updates the steering decision (dir_auto) to the Firebase Realtime Database.

2. ☁️ IoT Bridge (ESP8266)

  • Connects to the local WiFi and listens to changes in the Firebase Database.
  • Retrives the steering command (Left, Right, Forward, Stop).
  • Transmits the command to the Arduino via GPIO/Serial.

3. 🦾 Control Subsystem (Arduino)

  • Actuation: Controls DC motors via an H-Bridge driver based on signals from the ESP8266.
  • Security: Requires an authorized RFID Tag scan (MFRC522) to start the system.
  • Safety: Monitors battery voltage via analog pins and halts if levels are critical.

🛠️ Hardware Stack

  • Microcontrollers: Arduino Uno + ESP8266 (NodeMCU/WeMos).
  • Sensors: MFRC522 RFID, Voltage Sensors, IP Camera (Smartphone or ESP32-CAM).
  • Actuators: 4x DC Motors + Motor Driver (L298N).
  • Connectivity: WiFi (2.4GHz).

🚀 How to Run

  1. Cloud Setup: Create a Firebase Realtime DB and update credentials in esp_firebase.ino and lane_pilot.py.
  2. Firmware:
    • Upload arduino_main.ino to the Arduino.
    • Upload esp_firebase.ino to the ESP8266.
  3. Vision Engine:
    • Connect to the IP Camera stream.
    • Run the Python script: 
      python computer_vision/lane_pilot.py
  4. Start: Swipe the RFID card to unlock the car and watch it drive!

📸 Code Snippet (Lane Detection)

# From lane_pilot.py
def steering_angle(img, lines):
    # logic to calculate angle based on lane lines
    # ...
    angle_deg = int(angle_radian * 180.0 / math.pi)
    return angle_deg