Research on Road Environmental Sense Method of Intelligent Vehicle Based on Tracking Check

Environment perception is the premise for intelligent vehicles to drive safely and stably. Despite the rapid development of road detection technology based on visual images, it is still challenging to robustly identify road areas in visual images due to the influence of illumination changes and noise. In order to solve this problem, we introduce a new optimized lidar and camera sensor fusion method for road environment sensing of intelligent vehicles. In road boundary detection based on laser data, a median point filtering method of ordered pole cloud is proposed. A method of boundary search, boundary seed point growth and obstacle clustering is proposed to identify road boundary. In the lane line classification based on visual image, a lane line search classification method is proposed, which can effectively classify lane lines and extract single lane lines. On the basis of the optimization of sensors, several constraint conditions are proposed based on the fusion of the two data, and the location of missing lane lines is predicted by using the road information identified by lidar and image, and the lane lines are identified again. Finally, a large number of experiments are carried out on kitti-Road benchmark data set, and a test platform is built to verify the results of the identification method proposed in this paper in rainy day, cloudy day, night and other special scenarios. Experimental results show that this method is superior to existing methods.