Augmented Pure Pursuit for High-Performance Path Tracking in Formula Driverless

Abstract

Pure Pursuit (PP) is a widely adopted geometric path-tracking controller valued for its robustness, low computational cost, and ease of deployment. However, under aggressive driving conditions, tire slip and transient yaw dynamics alter the steering-to-curvature map, degrading tracking performance. This paper proposes a minimal modification of PP in which the nominal steering command is scaled by a single gain, increasing effective steering authority without introducing integral action, sideslip estimation, or additional dynamic compensation layers. The controller is evaluated in MATLAB/Simulink co-simulation with VI-CarRealTime using a high-fidelity model of the SGe-06 Formula Student vehicle developed at the University of Padua, with parameters tuned via NSGA-III by jointly optimizing RMS lateral error, RMS steering-rate demand, and lap time. The method is assessed under both ideal and realistic sensing conditions and compared against standard PP and a state-of-the-art sideslip-compensated approach. Results show that the gain-augmented formulation improves the trade-off between tracking accuracy, steering smoothness, and lap time in high-dynamic scenarios while preserving the simplicity and computational efficiency of Pure Pursuit.