A Combined Dynamic–Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle

In modern automotive engineering, accurate vehicle sideslip angle estimation is crucial for enhancing vehicle safety, performance, and driver comfort. This paper addresses the challenge of estimating sideslip angle, an essential parameter for advanced driver-assistance systems (ADAS) and autonomous driving technologies. This study introduces a combined dynamic–kinematic extended Kalman filter (DK-EKF) approach that leverages the strengths of both kinematic and dynamic models while mitigating their individual limitations. The proposed DK-EKF enhances observability in low yaw rate conditions, a common issue with kinematic models, and improves the robustness of dynamic models against parameter uncertainties. A validation is conducted through extensive experimental tests, demonstrating the DK-EKF’s superior performance in various driving scenarios. The results confirm the efficacy of the proposed method in providing reliable sideslip angle estimation.