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Advanced Lane Finding Project

In this project, the goal is to write a software pipeline to identify in a video the lane boundaries, determine the curvature of the road and offset of the vehicle from the center. Output is shown below:

The Project

The goals / steps of this project are the following:

  • Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
  • Apply a distortion correction to raw images.
  • Use color transforms, gradients, etc., to create a thresholded binary image.
  • Apply a perspective transform to rectify binary image ("birds-eye view").
  • Detect lane pixels and fit to find the lane boundary.
  • Determine the curvature of the lane and vehicle position with respect to center.
  • Warp the detected lane boundaries back onto the original image.
  • Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

Usage

Cloning the repository and running the notebook is all that's needed to make the project work

Files

  • The images for camera calibration are stored in the folder called camera_cal. The images in test_images are for testing the pipeline on single frames.
  • The examples of the output from each stage of your pipeline in the folder are saved in folder ouput_images. The video called project_video.mp4 is the video the pipeline works on.
  • advanced_lane_finding files contain the output. the mp4 is the original video and gif is displayed above:

Notebook

The Jupyter_Notebook_Advanced_Lane_Finding.ipynb notebook individually processes through each of the steps detailed above with accompanying images