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

TL;DR

RAGE is a real-time, sensor-efficient estimator for vehicle dynamics in autonomous race cars, using standard sensors to improve safety and performance at physical limits.

Contribution

It introduces a novel method that estimates vehicle velocity, tire slip angles, and lateral forces using only common sensors, avoiding costly specialized equipment.

Findings

Validated through high-fidelity simulations and real-world tests.

Demonstrated accurate estimation of vehicle lateral dynamics.

Applicable to autonomous race cars for improved safety and control.

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.