Autonomous racing is increasingly becoming a proving ground for autonomous vehicle technology at the limits of its current capabilities. The most prominent examples include the F1Tenth racing series, Formula Student Driverless (FSD), Roborace, and the Indy Autonomous Challenge (IAC). Especially necessary, in high speed autonomous racing, is the knowledge of accurate racecar vehicle dynamics. The choice of the vehicle dynamics model has to be made by balancing the increasing computational demands in contrast to improved accuracy of more complex models. Recent studies have explored learning-based methods, such as Gaussian Process (GP) regression for approximating the vehicle dynamics model. However, these efforts focus on higher level constructs such as motion planning, or predictive control and lack both in realism and rigor of the GP modeling process, which is often over-simplified. This paper presents the most detailed analysis of the applicability of GP models for approximating vehicle dynamics for autonomous racing. In particular we construct dynamic, and extended kinematic models for the popular F1TENTH racing platform. We investigate the effect of kernel choices, sample sizes, racetrack layout, racing lines, and velocity profiles on the efficacy and generalizability of the learned dynamics. We conduct 400+ simulations on real F1 track layouts to provide comprehensive recommendations to the research community for training accurate GP regression for single-track vehicle dynamics of a racecar.

翻译：自动驾驶赛车正日益成为测试自动驾驶技术在极限工况下性能的试验场，其中最著名的案例包括F1Tenth赛车系列赛、大学生无人驾驶方程式大赛（FSD）、Roborace以及Indy自动驾驶挑战赛（IAC）。在高速自动驾驶赛车中，精确掌握赛车动力学特性尤为关键。选择车辆动力学模型时，需在计算成本与模型复杂度带来的精度提升之间做出权衡。近期研究探索了基于学习方法，如高斯过程回归，来近似车辆动力学模型。然而，这些研究多聚焦于高层规划（如运动规划、预测控制），且对高斯过程建模过程的真实性和严谨性有所欠缺，常过度简化模型。本文首次系统分析了高斯过程模型在自动驾驶赛车动力学近似中的适用性，具体针对F1TENTH赛车平台构建了动力学模型与扩展运动学模型。我们系统研究了核函数选择、样本数量、赛道布局、赛车线及速度曲线对学习模型效能与泛化能力的影响。基于真实F1赛道布局开展了400余次仿真实验，为研究界提供了训练精确高斯过程回归模型以描述赛车单轨动力学的全面建议。