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Udacity Self Driving Course (Advanced Lane Line Tracking P4)
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camera_cal
examples
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README.md
challenge_video.mp4
example_writeup.pdf
harder_challenge_video.mp4
main.py
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README.md

Advanced Lane Finding

Udacity - Self-Driving Car NanoDegree

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.

Here is the video of Lane Tracking:

Full Video of Lane Tracking

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