Improved Path Tracking Optimization Method for Intelligent Vehicles Using Standard Wide-Angle Lenses

With the rapid development of autonomous driving technology, the path tracking capability of intelligent vehicles has become a focus of research. This paper addresses the issue of insufficient path tracking accuracy of intelligent vehicles using standard wide-angle CMOS cameras in complex track environments, proposing a method that combines perspective image processing and inverse perspective image processing (Combined Perspective Processing, CPR). This method first directly extracts the track edges from the original perspective image, preserving the detailed information and a wide effective field of view of the image. Then, by using inverse perspective Transformation processing, these edge coordinates are mapped to the coordinate system of the top-down view to enhance the recognition rate of track elements and the accuracy of path tracking. Experimental results show that compared to traditional methods, this method significantly improves path tracking error and element recognition accuracy, with high robustness and practical value.