Lane Detection Project with Carla Simulator

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This project demonstrates a lane detection system using the Carla autonomous driving simulator. The goal is to detect lane lines and steer the vehicle within the detected lane.

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Project Overview

The project consists of these main components:

• Carla Simulator Setup: The code connects to the Carla server, loads a map, spawns a vehicle, and attaches a camera sensor to capture images.
• Lane Detection: Camera images from the vehicle’s perspective are processed to detect lane markings.
• Perspective Transformation: Images are transformed into a bird-eye view to make lane detection easier.
• Sliding Window Technique: A sliding window algorithm is applied to locate lane pixels in the processed images.
• Polynomial Fitting: Detected lane pixels are fitted to a second-degree polynomial representing lane lines.
• Curvature Calculation: The curvature of the lane lines and the vehicle’s position within the lane are calculated.
• Vehicle Control: Steering and speed of the vehicle are controlled based on the detected lane to keep the vehicle centered.

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How It Works?

1. Simulation Setup:

   • The Carla client connects to the server at localhost:2000.
   • Weather and environmental conditions (cloudiness, precipitation, etc.) are set.
   • A vehicle is spawned at a predefined point in the simulation.

2. Camera Setup:

   • An RGB camera sensor is attached to the vehicle to capture front-facing images.

3. Lane Detection:

   • Images are transformed into a top-down view.
   • The sliding window method is used to identify lane pixels.
   • A second-degree polynomial curve is fitted to the lane pixels.
   • The curvature of the lane is calculated, showing how sharply the lane is curving.

4. Vehicle Control:

   • Based on the detected lane, the vehicle’s position is adjusted.
   • The vehicle receives steering, throttle, and brake commands to stay centered in the lane.
   • The vehicle will turn left, right, or go straight based on the lane curvature.

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How To Run

1. Requirements:

   • Carla Simulator (Make sure the server is running on localhost:2000).
   • Python dependencies: cv2, carla, numpy, and other necessary libraries.

2. Steps:

   • Start the Carla simulator.
   • Run the main Python script:

     python main.py

   • The vehicle will start driving in the Carla environment, and the lane detection system will guide it based on the detected lane lines.

3. Stopping the Program:

   • Press q on the OpenCV window to stop the program.

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Key Functions

• get_perspective_matrices(): Applies perspective transformation to make lane detection easier.
• hist(): Computes a pixel intensity histogram to identify lane lines.
• find_lane_pixels(): Detects lane pixel points using a sliding window technique.
• fit_poly(): Fits a second-degree polynomial curve to the detected lane pixel points.
• measure_curvature(): Calculates the curvature of lane lines.
• plot(): Draws lane lines on the output image and applies vehicle control based on lane detection.

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Feel free to customize and expand this lane detection system in your own Carla simulation projects!

https://github.com/koesan/Autonomous_Driving_With_Lane_Detection