Control strategies for autonomous vehicle path tracking: A comparative study of PID, Pure-Pursuit, and Stanley methods

Abstract

Effective control strategy selection is crucial for safe and efficient autonomous vehicle navigation, a key aspect of robotics. This study compares three control strategies: Proportional-Integral- Derivative (PID) control, Pure-Pursuit, and Stanley. Each control strategy is tested in the Carla simulator using the kinematic bicycle representation combined with sensor inputs from GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit), Performance is evaluated on three benchmark trajectories, assessing Mean Absolute Cross-track Error (MCTE), Steering Effort (SE), and Mean Steering Effort (MSE). Results indicate that Stanley consistently outperforms PID and Pure-Pursuit regarding accuracy and responsiveness. This analysis guides the selection of suitable control strategies for autonomous vehicles.