Autonomous racing control is a challenging research problem as vehicles are pushed to their limits of handling to achieve an optimal lap time; therefore, vehicles exhibit highly nonlinear and complex dynamics. Difficult-to-model effects, such as drifting, aerodynamics, chassis weight transfer, and suspension can lead to infeasible and suboptimal trajectories. While offline planning allows optimizing a full reference trajectory for the minimum lap time objective, such modeling discrepancies are particularly detrimental when using offline planning, as planning model errors compound with controller modeling errors. Gaussian Process Regression (GPR) can compensate for modeling errors. However, previous works primarily focus on modeling error in real-time control without consideration for how the model used in offline planning can affect the overall performance. In this work, we propose a double-GPR error compensation algorithm to reduce model uncertainties; specifically, we compensate both the planner's model and controller's model with two respective GPR-based error compensation functions. Furthermore, we design an iterative framework to re-collect error-rich data using the racing control system. We test our method in the high-fidelity racing simulator Gran Turismo Sport (GTS); we find that our iterative, double-GPR compensation functions improve racing performance and iteration stability in comparison to a single compensation function applied merely for real-time control.

翻译：自主赛车控制是一个具有挑战性的研究问题，因为车辆被推至操控极限以实现最优单圈时间，从而呈现出高度非线性和复杂的动力学特性。漂移、空气动力学、底盘重量转移和悬架等难以建模的影响可能导致不可行或次优的轨迹。虽然离线规划能够针对最小单圈时间目标优化完整参考轨迹，但此类建模误差在使用离线规划时尤为有害，因为规划模型误差与控制器建模误差会叠加。高斯过程回归（GPR）可用于补偿建模误差。然而，以往研究主要关注实时控制中的建模误差，未考虑离线规划所用模型如何影响整体性能。本文提出一种双GPR误差补偿算法以降低模型不确定性；具体而言，我们分别使用两个基于GPR的误差补偿函数对规划器模型和控制器模型进行补偿。此外，我们设计了一个迭代框架，利用赛车控制系统重新收集富含误差的数据。我们在高保真赛车模拟器Gran Turismo Sport（GTS）中测试了该方法，发现与仅用于实时控制的单一补偿函数相比，我们的迭代双GPR补偿函数能提升赛车性能并改善迭代稳定性。