RAGE: A Tightly Coupled Radar-Aided Grip Estimator For Autonomous Race Cars

Abstract

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.

Figures (9)

• Figure 1: Unimore Racing’s Dallara EAV-24 during the 2024 Abu Dhabi Autonomous Racing League at Yas Marina F1 circuit.
• Figure 2: Schematic representation of the dynamic single-track model.
• Figure 3: Sensor setups for the Dallara EAV-24. While the car is equipped with additional sensors, only the ones used in this study are shown.
• Figure 4: Simulated double lane change maneuver. The simulator provides slip angles and lateral forces for all four tires. For comparison with the single-track model, slip angles are averaged and lateral forces summed per axle.
• Figure 5: Simulated spin on wet asphalt ($\mu = 0.6$), the car brakes from 65 m/s before a left turn, but reduced grip causes rear instability and a 135° spin.