Algorithm on lane changing and tracking control technology for Intelligent Vehicle

Preprocess the lane gray image to obtain binary image and propose an improved Hough transformation algorithm to obtain the feature parameter of the road edge in the binary image. According to the prediction result of the Kalman filtering, we establish the area of interest (AOI) of the road edge and adjust the size of AOI dynamically to track the road edge accurately. To guarantee Intelligent Vehicle tracks preconcerted path steadily, controller design is one of key technologies. Establish vehicle kinematics model firstly, construct state space mathematics model of Intelligent Vehicle tracking path closed loop control system by adopted vehicle position deviation. Based on Backstepping function control algorithm, choose Lyapunov function and design controller for Intelligent Vehicle lane changing, overtaking and tracking. Simulation and experiment result show that the controller designed by this method can track preconcerted path quickly, it is steady and reliable during Intelligent Vehicle lane changing and overtaking control course.