RAGE-XY: RADAR-Aided Longitudinal and Lateral Forces Estimation For Autonomous Race Cars

TL;DR

RAGE-XY is a real-time vehicle force estimation framework for autonomous race cars that uses onboard sensors and includes online RADAR calibration and an extended vehicle model.

Contribution

It introduces an extended RAGE framework with RADAR calibration and a tricycle vehicle model for improved force and velocity estimation.

Findings

Validated with high-fidelity simulations and real-world experiments.

Demonstrated improved accuracy and robustness in vehicle dynamics estimation.

Abstract

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.