The goals / steps of this project are the following:

• Make a pipeline that finds lane lines on the road
• Reflect on your work in a written report

My pipeline consisted of the following steps:

#	Description	Image
1	Original image.
2	Create and apply the mask to keep only yellow [128,128,0]..[255,255,100] (RGB range) and white [220,220,220]..[255,255,255] pixels. This allows to keep the yellow lines from disappearing on the bright background when converted to grayscale.
3	Convert image to grayscale.
4	Apply Gaussian blur to suppress noise and spurious gradients.
5	Apply Canny edge detector.
6	Apply the region of interest mask.
7	Use the Hough transform to find lines from Canny edges.
8	Filter out lines from the Hough transform with slopes not relevant to lane lines we are trying to detect.

In order to draw a single line on the left and right lanes, I:

1. Partitioned to left/right the output of filtered Hough transform by horizontal location relative to the center of the image;
2. Fit a 1st-degree polynomial to points in each partition to get the slope/intercept of the fitted line;
3. Took simple moving average of the last 5 slopes/intercepts;
4. Plotted the resulting left and right lines on the input image.

• Might not work under different light conditions.
• Will not perform great on curved lane marks.
• If lane marks are missing in the multiple frames, will not keep the last values.

• Try various color space transformations for difficult light conditions.
• Use higher degree polynomial to fit curved lane marks.
• Handle case when lane marks disappear in multiple consecutive frames by filling the gap with the last values.