Autonomous race cars, such as in Formula Student Driverless, operate close to their physical handling limits. The resulting highly nonlinear vehicle behavior increases the path tracking complexity, especially on narrow tracks. Model Predictive Control (MPC) is commonly used to address this issue, a method whose performance is closely tied to the accuracy of the underlying prediction model. This paper presents a novel, real-time capable prediction model for autonomous race cars that adjusts to changing conditions by combining information from past runs and the current driving situation. Our model is divided into three consecutive submodels: a nominal Kinematic Bicycle Model, an offline Bayesian Linear Regression (BLR) model, and an online Sparse Gaussian Process Regression (SGPR) model. The proposed approach enables efficient integration of all available data without significantly increasing computational cost, ensuring high prediction accuracy and a quantitative uncertainty assessment right from the start of the run. Compared to existing approaches, an improvement in prediction accuracy of up to 57% was achieved. Further, we successfully demonstrated the practical applicability of the model within an MPC-based path tracking controller on a real Formula Student race car.

翻译：自动驾驶赛车（例如大学生无人驾驶方程式赛车）需要在其物理操控极限附近运行。由此产生的高度非线性车辆行为增加了路径跟踪的复杂性，尤其是在狭窄赛道上。模型预测控制（MPC）常用于解决此问题，该方法的性能与底层预测模型的精度密切相关。本文提出了一种新颖的、具备实时能力的自动驾驶赛车预测模型，该模型通过结合过往运行数据与当前驾驶情境来调整变化条件。我们的模型分为三个连续的子模型：名义上的运动学自行车模型、离线贝叶斯线性回归（BLR）模型和在线稀疏高斯过程回归（SGPR）模型。所提出的方法能够在不过多增加计算成本的情况下高效整合所有可用数据，从而确保从运行开始就具备高预测精度和定量的不确定性评估。与现有方法相比，预测精度最高提升了57%。此外，我们成功地在基于MPC的路径跟踪控制器中，于一台真实的大学生方程式赛车上验证了该模型的实用适用性。