Identifying lanes on the road is a common task performed by all human drivers to ensure their vehicles are within lane constraints when driving, so as to make sure traffic is smooth and minimise chances of collisions with other cars in nearby lanes. Similarly, it is a critical task for an autonomous vehicle to perform. It turns out that recognising lane markings on roads is possible using well known computer vision techniques. We will cover how to use various techniques to identify and draw the inside of a lane, compute lane curvature, and even estimate the vehicle's position relative to the center of the lane. The first step we will take is to find the calibration matrix, along with distortion coefficients for the camera that was used to take pictures of the road.

You can find my code here. It might be more up to date than the article;). This is the fourth article in the self driving cars series. If you want to know why I'm sharing this and more about my journey, please read this. For each image of the given video, we are going to detect the driving lane lines on each side of the car and visually highlight the area between them.