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 & tables
Fig. 1: Unimore Racing’s Dallara EAV-24 during the 2024 Abu Dhabi Autonomous Racing League at Yas Marina F1 circuit. Photo by Ian Bundey.
Fig. 2: Schematic representation of the dynamic single-track model.
Fig. 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.
Fig. 4: Simulated double lane change maneuver. The simulator outputs slip angles and lateral forces for all four tires. For the single-track model, slip angles are averaged and forces summed per axle.
Fig. 5: Simulated spin on wet asphalt ( μ=0.6 ), the car brakes from 65 m/s before a left turn, but reduced grip causes rear instability and a 135° spin.
Fig. 6: Estimated Pacejka peak coefficient D under three different road friction levels.
Fig. 7: Front and rear tire Pacejka model fitted over time. The sequence of model parameters captures the changing tire behavior and lateral dynamics throughout the lap.
Fig. 8: Estimated lateral forces during the hot-lap. RAGE tracks lateral forces accurately, with minor errors due to neglecting the limited-slip differential and combined slip effects.
Fig. 9: RAGE outputs compared with reference data obtained from the optical sensor, covering a complete lap of the Yas Marina F1 track. The lateral acceleration estimate is computed combining the front and rear lateral tire forces obtained from the Pacejka model with the online-estimated parameters.
Fig. 10: The estimated tire cornering stiffness shows a correlation with tire temperature and pressure, and exhibits periodic variations aligned with the circuit layout. The color-coded stiffness values correspond to specific segments of the track layout displayed on the right.