Sam's space

The fourth project in the self-driving car course was a reiteration and improvement of the lane finding project that we did earlier.

The goal was to learn the very basics of computer vision. This includes learning about

• camera calibration using a set of chessboard images, like this:

• distortion correction using the calculated distortion of the camera,
• color thresholding, using various color spaces, to obtain a “binary” image which only shows pixels which are likely part of a lane line, so that from the image below,

we get an output like this one:

• perspective transformation for getting a birds-eye view of the road (so that lane lines are parallel in the transformed image),

• detecting lane line pixels on the transformed image using pixel-density histograms and the sliding-window method (identifying areas dense in pixels based on the lower half of the image, and moving upwards in a sliding method, to see where the lanes go)
• determining the fane curvature and the car’s position relative to the center of the lane,
• outputting a visugl estimation of the lane by warping back the calculated area to the original image. The final output looks something like this: