One of the most popular techniques for road lane detection is computer vision. It involves using cameras mounted on the vehicle to capture images of the road and analyzing those images to detect the lane markings. The process of lane detection typically involves several steps, including image preprocessing, edge detection, and lane fitting. In the image preprocessing stage, the image is transformed to enhance the features of the lane markings. This may involve adjusting the brightness and contrast of the image, removing noise, and smoothing the edges of the lane markings. Next, the edge detection algorithm is applied to identify the edges of the lane markings. The most commonly used edge detection algorithm is the Canny edge detector, which identifies the sharp changes in intensity in the image and marks them as edges.
Once the edges are detected, the lane fitting algorithm is used to fit a model to the edges and determine the position and orientation of the lane markings. The most popular lane fitting algorithms are the Hough transform and the RANSAC algorithm. Finally, the detected lanes are tracked over time to ensure that the vehicle stays in its designated lane. This involves using algorithms to predict the position of the lanes in the next frame based on their position in the current frame.