An accurate vehicle driving state observer is a necessary condition for a safe automotive electronic control system. Vehicle driving state observer is challenged by unknown measurement noise and transient disturbances caused by complex working conditions and sensor failure. For the classical adaptive unscented Kalman filter (AUKF) algorithm, transient disturbances will cause the failure of state estimation and affect the subsequent process. This paper proposes an AUKF based on a modified Sage–Husa filter and divergence calculation technique for multi-dimensional vehicle driving state observation. Based on the seven-degrees-of-freedom vehicle model and the Dugoff tire model, the proposed algorithm corrects the measurement noise by using modified Sage–Husa maximum posteriori. To reduce the influence of transient disturbance on the subsequent process, covariance matrix is updated after divergence is detected. The effectiveness of the algorithm is tested on the double lane change and Sine Wave road conditions. The robustness of the algorithm is tested under severe transient disturbance. The results demonstrate that the modified Sage–Husa UKF algorithm can accurately detect transient disturbance and effectively reduce the resulted accumulated error. Compared to classical AUKF, our algorithm significantly improves the accuracy and robustness of vehicle driving state estimation. The research in this paper provides a reference for multi-dimensional data processing under changeable vehicle driving states.

Complex state estimation is facing urgent needs currently. Simply applying one state estimation method is not accurate and robust enough to deal with future state estimation tasks, which may lead to severe trouble for any system. This paper proposes a novel multi-filter selection algorithm based on the Akaike information criterion and Roulette wheel, named as ARMFS algorithm. Unscented Kalman Filter (UKF) and Elman neural network aided UKF (ENN-UKF) are selected as two base filters. The proposed ARMFS algorithm is tested in the field of vehicle driving state estimation. The experimental results demonstrate that the ARMFS algorithm can intelligently select between two base filters with a general accuracy of 71.24%, which is high enough to provide an optimized filter result. This study provides a reference and works as an attempt in applying multi-filter selection algorithm to complex state estimation.