An Accurate Path Tracking Algorithm for Autonomous Vehicles Based on Pure Pursuit with Systematically Designed Look-Ahead Distance and Sideslip Compensation

Pure pursuit is still being utilized as a path tracking algorithm for autonomous vehicles due to its advantages, such as robustness and easy implementation. However, using an inappropriate look-ahead distance and ignoring the effects of dynamics deteriorate the path tracking performance of pure pursuit. To improve the tracking performance, we propose a pure pursuit-based path tracking algorithm that quickly reduces tracking error and considers the sideslip by dynamics. For fast tracking error reduction, a dynamic look-ahead distance is designed as a function of speed and path curvature by analyzing the tracking error change according to the look-ahead distance. In addition, to compensate for the sideslip effect, the desired steering angle is calculated using the sideslip integrated-pure pursuit geometry. Simulation results show that the proposed algorithm significantly reduced the tracking error compared to the classical pure pursuit at a maximum 70 km/h speed. Also, the proposed method outperformed existing improved pure pursuits.