Vision-Based Intelligent Vehicle Road Recognition and Obstacle Detection Method

With the development of the world economy and the accelerating process of urbanization, cars have brought great convenience to people's lives and activities, and have become an indispensable means of transportation. Intelligent vehicles have the important significance of reducing traffic accidents, improving transportation capacity and broad market prospects, and can lead the future development of the automotive industry, so they have received extensive attention. In the existing intelligent vehicle system, the laser radar is a well-deserved protagonist because of its excellent speed and precision. It is an indispensable part of achieving high-precision positioning, but to some extent, the price hindering its marketization is a major factor. Compared with lidar sensors, vision sensors have the advantages of fast sampling rate, light weight, low energy consumption and low price. Therefore, many domestic and foreign research institutions have listed them as the focus of research. However, the current vision-based intelligent vehicle environment sensing technology is also susceptible to factors such as illumination, climate and road type, resulting in insufficient accuracy and real-time performance of the algorithm. This paper takes the environment perception of intelligent vehicles as the research object, and conducts in-depth research on the existing problems in road recognition and obstacle detection algorithms, including road image vanishing point detection, road image segmentation problem, road scene based on binocular vision. Three-dimensional reconstruction and obstacle detection technology.