Reliable vehicle sideslip angle fusion estimation using low-cost sensors

This paper proposes an adaptive hybrid fusion estimation strategy using low-cost sensors to estimate vehicle sideslip angle in a wide driving-maneuver range. First, the kinematics model-based extended Kalman filter (KEKF) is designed as the basic filtering framework. To ensure the KEKF accuracy and observability in a wide range of driving maneuvers, the influence of inertial sensor drift is considered and the estimation from the bicycle dynamics-based extended Kalman filter (DEKF) is introduced as the KEKF measurement. In the DEKF, the cornering stiffness estimation algorithm is developed to adapt to the changes in tire-road conditions. Further, to reduce the adverse impact of the DEKF performance degradation in nonlinear regions caused by severe maneuvers, a fuzzy decision module is proposed to determine the degree that the KEKF utilizes the DEKF estimation as the reliable measurement. Finally, the sequential measurement-update processing algorithm is developed and the adaptive weighted fusion algorithm is executed to realize the global fusion. The results of both intensive simulations and experiments validate the feasibility and effectiveness of the proposed strategy.