Accurate real-time state parameters are critical for vehicle control systems, but direct measurement is often hindered by sensor inaccuracies and high costs. This study proposes a dual-layer estimation framework using unscented Kalman filters. The first layer estimates vehicle velocities by fusing inertial measurement unit data with either global navigation satellite system signals or a dynamics model, adaptively adjusting for signal reliability. The second layer simultaneously estimates tire-road friction coefficients and tire forces by integrating a vehicle dynamics model with a combined-slip tire model. Simulations under varying signal conditions demonstrate the method’s effectiveness in estimating key parameters—velocities, friction coefficients, and tire forces—enhancing robustness for real-time vehicle control.
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