RPi-based Lane-Assist Autonomous Car

This project implements a lane-assist autonomous car using a Raspberry Pi and YOLO for object detection, lane detection, and motor control. The car autonomously navigates and avoids obstacles using computer vision.

Features:

• Lane Detection: Detects and follows the road using edge detection and Hough Line Transform.
• Object Detection: Identifies objects in the lane and stops the car if an obstacle is detected.
• Motor Control: Adjusts the motor speeds to keep the car in the lane and avoid collisions.
• Autonomous Navigation: Real-time feedback is provided, and the car can turn and stop based on detected lanes and objects.

Hardware Requirements:

• Raspberry Pi 5
• PiCamera for image capture
• Motors and motor controller
• YOLO object detection model for real-time object classification

How to Use:

1. Clone or download this repository.
2. Install the required libraries:
   • cv2 (OpenCV)
   • ultralytics (YOLO)
   • gpiozero (motor control)
3. Set up the Raspberry Pi and connect the motor controller and camera.
4. Run the main Python script (lane_assist_car.py).
5. The car will start autonomous navigation based on lane detection and obstacle avoidance.

Model:

The YOLO model is loaded and used in real-time to detect objects and stop the car if necessary.