A Study of Reinforcement Learning Techniques for Path Tracking in Autonomous Vehicles

Robust and accurate path tracking for autonomous vehicle navigation is a complex task, especially when it comes to managing system uncertainties such as inertia, slippage, and action delays. Although model-based controllers are efficient, their performance can be limited by such uncertainties and by the complexity of the gain tuning process. To address this, our study evaluates the effectiveness of four strategies using reinforcement learning (RL) with a controller, to provide either - steering correction, full gain tuning, gain correction, or end-to-end learning without any controller - to improve trajectory tracking. These methods are trained on geometric controllers (Pure Pursuit, Stanley) and model predictive controllers (Romea, EBSF). Our results show that all RL methods improve tracking at high speeds, with steering correction proving the most consistently effective in all cases.