Lane Finding Project for Self-Driving Car ND
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debug_images
test_images
test_results
P1.ipynb
P1_example.mp4
README.md
challenge.mp4
extra.mp4
laneLines_thirdPass.jpg
line-segments-example.jpg
raw-lines-example.mp4
solidWhiteRight.mp4
solidYellowLeft.mp4
white.mp4
yellow.mp4

README.md

#Finding Lane Lines on the Road

I used the following sequence of steps to arrive at the solution

  1. Grayscale Image

Gray Image

  1. Guassian Blur

Guass Blur Image

  1. Canny Edge Detection

Canny Edge Detection Image

  1. Region of Interest

ROI Image

  1. Hough Transformation and Extrapolation

Hough Image

Hough Image

Some lessons learnt from experience

  1. I use Anaconda on windows. Had some difficulty in installing ffmpeg. I used the guideline provided in the following link to solve the issue https://github.com/adaptlearning/adapt_authoring/wiki/Installing-FFmpeg

  2. I used a debug folder to save all intermediate image. This helped me a lot in easy tuning of various parameters.

  3. I did not attempt the Optional challenge to optimize available time with me. Test runs with current code was not successful.

  4. In terms of improvement I could further smooth the lines across frames in video.

  5. I did not have experience with Jupyter. It is very different for a coding environment. However after using it I could visibly see the advantage of such an environment.

  6. Line Extrapolation - I used y=mx+c draw the extrapolated line (First identify slope with co-ordinates ((y2-y1)/(x2-x1)), Second calculate C and Third identify new co-ordinates based on given y-axis)

Results uploaded to Youtube

Video White https://youtu.be/D7xby1-8GI0

Video Yellow https://youtu.be/-qIGKi5mOCA