A Control Strategy for Improving the Accuracy of Lateral Tracking of Autonomous Vehicles

Abstract Lateral tracking is one of the key technologies for autonomous vehicles. It is particularly important when vehicles are traveling at low speeds in narrow and complex scenes, such as parking lots and alleys. In order to improve the effective and accurate control of the traditional Pure Pursuit algorithm (PP) in the lateral tracking of low-speed autonomous vehicles, a continuous adaptive piecewise fitting method based on cubic Bezier curve was proposed to smooth the reference path, and then search the look-ahead point according to the geometric relationship between the vehicle and the reference path: An improved search algorithm based on vehicle speed and road curvature was used to search the look-ahead point. If the reference path curvature changes too much, the preliminarily obtained look-ahead point are modified, and finally the improved control algorithm (Improve Pure Pursuit Method (IPP) was obtained to calculate the steering Angle of the front wheel. Subsequently, the Carsim/Simulink co-simulation model and the autonomous vehicle platform were established for verification. Simulation and real Vehicle verification show that, compared with the pure pursuit algorithm based on Vehicle speed control (VPP) and Fuzzy Logic control pure Pursuit (FPP), IPP can perform better in complex low-speed lateral tracking scenes, reduce the problem of cutting-corner when the vehicle enters a corner, and improve the lateral tracking accuracy.