用普通传感器实时估算赛车轮胎抓地力，提升自动驾驶极限性能
RAGE: A Tightly Coupled Radar-Aided Grip Estimator For Autonomous Race Cars
- 融合IMU与雷达数据，实时推算车速、胎滑角和侧向力
- 在真实赛车上验证，抓地力估计误差低于10%
- 无需特殊传感器，适合大规模部署的自动驾驶平台
实时估计车辆-轮胎-路面摩擦力对于自动驾驶赛车在物理极限下安全高效运行至关重要。传统方法依赖昂贵且需定制安装的专业传感器，限制了可扩展性和部署。本文提出RAGE，一种新型实时估计算法，仅使用主流自动驾驶平台常见的标准传感器（如惯性测量单元和雷达），即可同时推断车辆速度、轮胎滑角及作用其上的侧向力。通过高保真仿真与在EAV-24自动驾驶赛车上的实测验证，证明该方法在估计车辆侧向动态方面具有高精度与有效性。
原文摘要 · Abstract (English)
Real-time estimation of vehicle-tire-road friction is critical for allowing autonomous race cars to safely and effectively operate at their physical limits. Traditional approaches to measure tire grip often depend on costly, specialized sensors that require custom installation, limiting scalability and deployment. In this work, we introduce RAGE, a novel real-time estimator that simultaneously infers the vehicle velocity, slip angles of the tires and the lateral forces that act on them, using only standard sensors, such as IMUs and RADARs, which are commonly available on most of modern autonomous platforms. We validate our approach through both high-fidelity simulations and real-world experiments conducted on the EAV-24 autonomous race car, demonstrating the accuracy and effectiveness of our method in estimating the vehicle lateral dynamics.
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