This project is based on Udacity's Self Driving car Nanodegree

My pipeline consisted of 5 steps:

1. Convert the image to grayscale and apply a Gaussian blur. For the parameters, I used kernel_size = (5, 5).

2. Apply Canny edge detection. In line with the recommended 3:1 ratio of thresholds, I used low_threshold = 40 and high_threshold = 120.

3. Apply a mask to the image outputted by Canny image detection. I applied a simple mask of trapezoid shape.

4. Apply the Hough Transform to the masked image from step 3.

5. Process and filter the lines found by the Hough Transform.

Steps 4 and 5 were performed in an iterative fashion. The Hough Transform parameters were initialized. Since the output of the Hough Transform is a potential candidates named lines, I extended these lines to the top and bottom of the masked image region. The lines were also sorted as per their length. I created a virtual zone for acceptable x coordinates of the endpoints of the line, and I removed entries in the lines list whose endpoints fell outside these intervals. I declared the longest line in lines to be one of the two lane lines, and for the other I took the longest remaining line whose slope was of the opposite sign as that of lines[0].

If this process did not yield two lines, then I altered the parameters for hough transform and repeated steps 4 and 5.

This is a simple lane detection pipeline, and as such it has a number of potential shortcomings.

• First, since we are fitting lines (not curves) to the lane lines, this pipeline may yield poor results on urban roads with sharp turns.
• Second, it may struggle when there are signs in the road, such as arrows etc.
• This pipeline may not be robust to different lighting.

To more accurately detect the lane lines, it would be beneficial to fit a Clothoid curve curve to the lanes, instead of fitting a line.