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 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 zero-shot generalizes to new racetracks.
翻译:自动赛车控制器的开发具有挑战性,因为赛车的车手在车身限制下运行。由于要求提高性能,自动赛车研究发现机器学习控制器的激增。虽然这些方法显示有竞争性的性能,但其实际适用性往往有限。残余政策学习承诺通过将古典控制器与有学识的残余控制器相结合来缓解这种情况。残留控制器的关键优势是它们具有与古典控制器稳定行为平行的高适应性。我们建议为自动赛车设置一个剩余车辆控制器,该控制器将学会为追逐赛道而修正古典控制器。在一项广泛的研究中,我们方法的绩效收益被评价为F1THETH自动赛车系列的模拟车。对12个复制的真实世界赛道的评价显示,残余控制器比古典控制器平均减少4.55%的时程,零弹射一般将减少新的赛道。