RAGE-XY: RADAR-Aided Longitudinal and Lateral Forces Estimation For Autonomous Race Cars
This work addresses the need for precise force estimation in autonomous race cars, but it is incremental as it builds upon an existing method with specific enhancements.
The authors tackled the problem of estimating vehicle velocity, tire slip angles, and forces for autonomous race cars using onboard sensors like IMUs and RADARs, by extending a previous framework with online RADAR calibration and a tricycle model, resulting in improved accuracy and robustness in lateral and longitudinal dynamics estimation as validated in simulations and real-world experiments.
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.