Vision Based Lane Detection for Self-Driving Car

Lane detection plays a key role in building intelligent traffic system. How to improve the accuracy of lane recognition and the ability of curve detection has always been the focus of research. Aiming at the detection of yellow and white lane lines and curves in complex environments, this paper introduces a new lane line detection method based on machine vision, which combines the yellow lane line processed in HSV space with the white lane line processed in grayscale space. Through canny edge detection, inverse perspective transformation and sliding window polynomial fitting method to achieve real-time lane detection. The experiment shows that the algorithm can accurately detect the curve and the lane line under the light changing circumstances, and can calculate the vehicle's deviation distance according to the lane line detection results. At the same time, the algorithm shows good reliability and robustness under multiple working conditions.