An All-Weather Lane Detection System Based on Simulation Interaction Platform

Computer vision is an important part of the autonomous vehicles to gain the perception of the surrounding environment. In order to satisfy the adaptability of lane detection system under different illumination and different road conditions, an effective lane detection method based on image classification and hybrid isomeric operators is proposed. Camera correction matrix and distortion coefficient are obtained by using checkerboard grid images, and they will be cached to be shared with subsequent image streams to improve the speed of real-time lane detection. In the process of preprocessing, the images under different illumination conditions are classified and processed, and the inverse perspective transformation is used to deal with the 2-D image flow so that the image flow has a certain depth of data. According to the intensity of the whole gray mean, the edge detection is carried out by using heterogeneous operators and combining with a variety of threshold filtering methods to extract the lane pixels. The lane detection module transmits the vehicle steering angle and the deviate distance from the road center line to the decision layer, effectively implementing the interactive simulation. Experiments show that the algorithm has good real-time performance, stability, and robustness under different illumination and road conditions.