RAGE: A Tightly Coupled Radar-Aided Grip Estimator For Autonomous Race Cars
This addresses the need for scalable and cost-effective grip estimation in autonomous racing, though it is incremental as it builds on existing sensor-based methods.
The paper tackled the problem of real-time vehicle-tire-road friction estimation for autonomous race cars by introducing RAGE, a novel estimator that uses standard sensors like IMUs and RADARs, achieving accurate lateral dynamics estimation validated in simulations and real-world experiments on the EAV-24 race car.
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.