An Algorithm of Curved Path Tracking with Prediction Model for Autonomous Vehicle

The Stanley method is a popular geometric steering controller. However, the Stanley method is not as robust to disturbances. The path tracking of the Stanley method is prone to large deviations in the curved path. In this paper, a predictive method is proposed to improve the curve tracking effect of the Stanley method. The predictive method of curved path tracking not only the relative position of the closest point of the path, but also the direction of the fixation point. Firstly, we use the inertial navigation system of autonomous vehicle to collect waypoints. Considering that the GPS signal is susceptible to environmental disturbances, we use cubic B-spline interpolation to correct the waypoints and get an accurate road map. Secondly, we calculate the position of the preview point by introducing the logarithmic relationship between the vehicle speed and the fixation point. Then, the preview point is used as the target for Stanley path tracking. The results in a variety of environments show the effectiveness of the proposed method.