chipmunk-pixel / carnd-lanelines-p1

Lane Finding Project for Self-Driving Car ND

Reflection

1. Describe your pipeline. As part of the description, explain how you modified the draw_lines() function.

The process for my pipeline is in two stages:

1. Detect lanes without lines drawn in
2. Draw in/extrapolate the lane lines

With the first stage the steps are:

1. Firstly process using median blur to get rid of anything noisy
2. Convert anything that "looks" like yellow to white, to detect yellow lines better
3. Convert to grayscale
4. Apply gaussian blur
5. Apply canny edge detection
6. Subset by region, estimated by a trapezoid
7. Apply hough transform

With drawing in the lines, I used the output from the previous stage

1. Taking only the hough transform, repeat with canny, being more aggressive with threshold
2. Dialate the solution to get "solid" lines
3. Repeat with canny again
4. Use hough transform to get the lines
5. Use a modified draw_lines function draw in and extrapolate the lines.

The new draw_lines function:

1. Uses clustering to determine which sets of points belongs with which "line"
2. Uses cv2.fitLine to fit a line
3. Draws it based on the fitted line.

2. Identify potential shortcomings with your current pipeline

The lane line markings are not as smooth as the example, and have a tendency of jumping all over the place.

3. Suggest possible improvements to your pipeline

Since we use clustering iteratively to determine the line segment groupings it sometimes gets it wrong. It might be better to either:

• Keep history, so that it gets better over time
• Use a rule based approach so that it is consistent

Generally understanding roughly the location of lines in the previous frame should help inform where new lines are now at!