用雷达辅助估算赛车的横向纵向受力，提升实时控制精度。
RAGE-XY: RADAR-Aided Longitudinal and Lateral Forces Estimation For Autonomous Race Cars
- 融合雷达与惯性传感器，在线校准误差，优化侧向速度估计。
- 从单轮模型升级为三轮模型，可同时估算前后轮纵向力。
- 在真实赛车上验证，对复杂工况更具鲁棒性，适合高速自动驾驶场景。
本文提出RAGE-XY，是RAGE框架的扩展版本，基于车载标准传感器（如IMU和雷达）实现实时车辆速度、轮胎滑移角及受力估计。相比原方法，新方案引入在线雷达校准模块，有效缓解传感器错位对侧向速度估计的影响。同时，将车辆模型由单轮近似拓展至三轮模型，实现了对后轮纵向轮胎力的估计，不仅涵盖侧向动态，还支持纵向力学分析。通过高保真仿真与真实世界实验（在EAV-24自动驾驶赛车上）验证，该方法显著提升了车辆动力学估计的准确性与鲁棒性。
原文摘要 · Abstract (English)
In this work, we present RAGE-XY, an extended version of RAGE, a real-time estimation framework that simultaneously infers vehicle velocity, tire slip angles, and the forces acting on the vehicle using only standard onboard sensors such as IMUs and RADARs. Compared to the original formulation, the proposed method incorporates an online RADAR calibration module, improving the accuracy of lateral velocity estimation in the presence of sensor misalignment. Furthermore, we extend the underlying vehicle model from a single-track approximation to a tricycle model, enabling the estimation of rear longitudinal tire forces in addition to lateral dynamics. We validate the proposed approach through both high-fidelity simulations and real-world experiments conducted on the EAV-24 autonomous race car, demonstrating improved accuracy and robustness in estimating both lateral and longitudinal vehicle dynamics.
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