Estimation of Road Friction Coefficient via the Data Enforced Unscented Kalman Filter

• Proposes a novel data-enforced unscented Kalman filter (DeUKF) that integrates historical driving data to correct tire model uncertainties, improving TRFC estimation accuracy. • Leverages Willems’ Fundamental Lemma to adaptively correct the vehicle dynamics model within the UKF framework, mitigating the adverse effects of model parameter uncertainties. • Validates the proposed method through real vehicle experiments, demonstrating its effectiveness and superiority over conventional model-based approaches. • Enhances the reliability of TRFC estimation for intelligent chassis systems, contributing to improved vehicle safety and dynamic stability.