动态调整赛车速度，提升高阶自动驾驶稳定性与竞速表现。
Pushing the Performance Limits in Autonomous Racing: Continuous Stability-Aware Adaptive Velocity Planning in Formula Student Driverless
- 通过车辆稳定性间接推导连续摩擦系数，避免直接估算轮胎摩擦。
- 实车测试中平均提升8%的竞速表现，十圈累计提速35%。
- 适合追求高速稳定驾驶的无人赛车系统开发者参考。
在自动驾驶竞速，特别是公式学生无人驾驶竞赛中，精确规划赛车目标速度对实现优异圈速和稳定驾驶行为至关重要。尤其在高速工况下，速度规划（VP）需实时考虑赛道布局、环境影响、机械公差及控制误差等多重因素，极具挑战性。本文提出一种新型自适应速度规划方法，通过间接从车辆稳定性推导出连续的缩放因子，反映有效胎-地交互作用并涵盖控制误差影响。基于此，构建连续摩擦图谱，作为鲁棒、自适应的速度优化基础，兼顾车辆与环境极限。该方法在真实公式学生赛车上验证，十圈平均圈速提升35%，较非自适应方法平均提高8%。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。