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carnd-advanced-lane-lines's Introduction

Advanced Lane Finding

Udacity - Self-Driving Car NanoDegree

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.

The images for camera calibration are stored in the folder called camera_cal. The images in test_images are for testing your pipeline on single frames.

To help the reviewer examine your work, please save examples of the output from each stage of your pipeline in the folder called output_images, and include a description in your writeup for the project of what each image shows. The video called project_video.mp4 is the video your pipeline should work well on.

Files

  • writeup_report.md : explanation of my solution
  • test_videos_output : lane detection outputs for test videos
  • solution.ipynb : my solution code to detect lanes
  • output_images : examples of my solution's outputs
  • camera_cal : images for camera calibration
  • test_images : test images
  • project_video.mp4 : test video

carnd-advanced-lane-lines's People

Contributors

baumanab avatar brok-bucholtz avatar citlaligm avatar harveenchadha avatar mreichelt avatar mvirgo avatar ryan-keenan avatar tanutarou avatar

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