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

2026-04-03

AI Summary

This study addresses the critical need for real-time estimation of tire–road friction coefficients under extreme driving conditions in autonomous racing. The authors propose a tightly coupled estimation algorithm that relies solely on standard onboard sensors—such as inertial measurement units (IMUs) and radar—and integrates multi-source sensor data with a vehicle dynamics model. This approach enables simultaneous estimation of vehicle velocity, tire slip angle, and lateral tire forces, achieving high-accuracy perception of lateral vehicle dynamics without requiring expensive specialized sensors. Consequently, the method significantly enhances system scalability and deployment practicality. Validation through high-fidelity simulation and real-world experiments on the EAV-24 platform demonstrates the accuracy and real-time performance of the proposed framework in estimating both friction coefficients and lateral dynamic states.

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.

Problem

• friction estimation
• autonomous race cars
• tire grip
• real-time estimation
• vehicle dynamics

Innovation

• RAGE
• radar-aided estimation
• tire grip estimation
• real-time vehicle dynamics
• autonomous race cars