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.
Figures & tables
Fig. 1: Unimore Racing’s Dallara EAV-24 during the 2024 Abu Dhabi Autonomous Racing League at Yas Marina F1 circuit.
Fig. 2: Estimator overview.
Fig. 3: Schematic representation of the hybrid tricycle model. For lateral force estimation, an equivalent single-track model is adopted, allowing the estimation of the lateral forces acting at the front and rear axles. For longitudinal force estimation, a tricycle model is considered instead, enabling the estimation of the longitudinal forces acting on the two rear wheels.
Fig. 4: Single-wheel model of vehicle.
Fig. 5: Simulated results of the online auto-calibration method during the initial straight-line driving segment. The measured estimate is biased due to incorrect rotation calibration, whereas the calibrated estimate rapidly converges to the ground truth value.