When we drive, we use our eyes to decide where to go. The lines on the road that show us where the lanes are act as our constant reference for where to steer the vehicle. Naturally, one of the first things we would like to do in developing a self-driving car is to automatically detect lane lines using an algorithm.

My pipeline consisted of 5 steps.

1. I converted the images to grayscale.
2. I applied Gaussian Smoothing to reduce the sharpness of the image.
3. Canny edge detection was used to select the stong gradient no matter the color is.
4. To focus only on the lane I had to apply region masking to remove any unwanted pixels and to be able to select the lane lines only.
5. Hough transform is applied to select the lane lines joining the points/pixels and drawing a line overlayed on it.

In order to draw a single line on the left and right lanes, I modified the draw_lines() function by caluclating the gradient/slope and the y-intercept to be able to diffrentiate between the left lin and the right line, after that I had to average the position of each line and extrapolate them to the top and the bottom of the lane.

Below is the output of each step in the image pipeline.

When I tried to apply the pipeline on the challenge video first without the draw line addition it seems that the shades and many other high gradient pixels other than the lane lines, appear in the canny edge detection and after adding the draw line function it no longer compiles giving error.

Detecting the lane lines with more curvature and be able to detect the lines even if shades occur.