Problem
Research questions and friction points this paper is trying to address.
tire force estimation
RADAR-aided sensing
vehicle dynamics
sensor misalignment
autonomous race cars
RAGE-XY: RADAR-Aided Longitudinal and Lateral Forces Estimation For Autonomous Race Cars
🤖 AI Summary
Accurately and efficiently estimating the longitudinal and lateral dynamic states—such as velocity, sideslip angle, and tire forces—of an autonomous racecar using only standard onboard sensors like IMUs and RADAR, particularly in the presence of sensor mounting misalignments, remains highly challenging. To address this, this work proposes the RAGE-XY framework, which extends the original RAGE approach by incorporating an online RADAR calibration module and upgrading the vehicle dynamics model from a single-track to a three-wheel representation. This enables, for the first time, joint high-precision estimation of both longitudinal and lateral tire forces using only conventional sensors. Comprehensive validation through high-fidelity simulation and real-world experiments on the EAV-24 platform demonstrates that the proposed method significantly enhances the accuracy and robustness of vehicle state estimation.
📝 Abstract
In this work, we present RAGE-XY, an extended version of RAGE, a real-time estimation framework that simultaneously infers vehicle velocity, tire slip angles, and the forces acting on the vehicle using only standard onboard sensors such as IMUs and RADARs. Compared to the original formulation, the proposed method incorporates an online RADAR calibration module, improving the accuracy of lateral velocity estimation in the presence of sensor misalignment. Furthermore, we extend the underlying vehicle model from a single-track approximation to a tricycle model, enabling the estimation of rear longitudinal tire forces in addition to lateral dynamics. We validate the proposed approach through both high-fidelity simulations and real-world experiments conducted on the EAV-24 autonomous race car, demonstrating improved accuracy and robustness in estimating both lateral and longitudinal vehicle dynamics.
Innovation
Methods, ideas, or system contributions that make the work stand out.
RADAR calibration
tricycle vehicle model
real-time force estimation
autonomous race car
sensor fusion
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