The development of vehicle controllers for autonomous racing is challenging because racing cars operate at their physical driving limit. Prompted by the demand for improved performance, autonomous racing research has seen the proliferation of machine learning-based controllers. While these approaches show competitive performance, their practical applicability is often limited. Residual policy learning promises to mitigate this drawback by combining classical controllers with learned residual controllers. The critical advantage of residual controllers is their high adaptability parallel to the classical controller's stable behavior. We propose a residual vehicle controller for autonomous racing cars that learns to amend a classical controller for the path-following of racing lines. In an extensive study, performance gains of our approach are evaluated for a simulated car of the F1TENTH autonomous racing series. The evaluation for twelve replicated real-world racetracks shows that the residual controller reduces lap times by an average of 4.55 % compared to a classical controller and even enables lap time gains on unknown racetracks.

翻译：自主赛车车辆控制器的开发极具挑战性，因为赛车在其物理行驶极限状态下运行。受性能提升需求的驱动，自主赛车研究领域涌现出大量基于机器学习的控制器。尽管这些方法展现出具有竞争力的性能，但其实际应用性往往受限。残差策略学习通过将经典控制器与学习的残差控制器相结合，有望缓解这一缺陷。残差控制器的关键优势在于其高适应性，同时保留了经典控制器的稳定行为特性。我们提出了一种面向自主赛车的残差车辆控制器，该控制器学习修正经典控制器在赛道线路径跟踪中的不足。通过广泛研究，我们在F1TENTH自主赛车系列仿真车辆上评估了该方法带来的性能提升。对十二条真实世界赛道复现环境的评估表明，与经典控制器相比，残差控制器平均圈速降低4.55%，甚至在未知赛道上也能实现圈速优化。