In autonomous racing, especially in competitions such as Formula Student Driverless, precise planning of the target velocity of a race car is crucial for competitive lap times and stable driving behavior. Especially at high speeds, Velocity Planning (VP) is a significant challenge as it has to be performed in real time, taking into account track layouts, environmental influences, mechanical tolerances, and the resulting control inaccuracies. In this paper, we present a novel approach to VP that dynamically adapts to such changing conditions. Instead of estimating the physical Tire-Road Friction Coefficient (TRFC), a continuous scaling factor is inferred indirectly from vehicle stability. This factor not only reflects the effective tire-road interaction but also captures effects of control inaccuracies. From this, we generate a continuous friction map, which serves as a robust, adaptive basis for computing the optimal target speed, accounting for both vehicle and environmental limits. Our proposed approach was evaluated on a real Formula Student race car, showing a lap time improvement of 35 % over ten laps and an average increase of 8 % compared to a non-adaptive approach.

翻译：在自动驾驶赛车中，尤其是在大学生方程式无人车等赛事中，精确规划赛车的目标速度对于实现具有竞争力的单圈时间和稳定的驾驶行为至关重要。尤其是在高速行驶时，速度规划面临重大挑战，因为它必须实时完成，同时考虑赛道布局、环境影响、机械公差以及由此产生的控制误差。本文提出了一种新颖的速度规划方法，能够动态适应此类变化条件。该方法并非直接估计物理轮胎-路面摩擦系数，而是通过车辆稳定性间接推断出一个连续缩放因子。该因子不仅反映了有效的轮胎-路面相互作用，还捕捉了控制误差的影响。据此，我们生成一个连续摩擦图，作为鲁棒且自适应的基础，用于在考虑车辆和环境极限的情况下计算最优目标速度。所提出的方法在一辆真实的大学生方程式赛车上进行了评估，结果显示，与十圈内的非自适应方法相比，单圈时间改善了35%，平均提升幅度达8%。