Abstract
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.
Highlights
According to the released Global Status Report on Road Safety 2018 [1] by WHO, the annual road traffic deaths reached 1.35 million in 2016
We focus on five parameters because these parameters are sensitive to changes and more state variables can be further calculated based on these five parameters
Since our approach mainly aims at the weak robustness and the slow adaptive speed of adaptive unscented Kalman filter (AUKF), we present a robustness test to compare the performances of two methods under severe disturbances
Summary
According to the released Global Status Report on Road Safety 2018 [1] by WHO, the annual road traffic deaths reached 1.35 million in 2016. An effective solution towards safe driving is a worldwide problem. The stability-control technique is one of the most important techniques in vehicle active security [2]. Since the 1990s, automotive electronics have developed rapidly. Many methods have been proposed to improve automotive safety, including anti-lock brake system (ABS) based on tire dynamic model [3], traction control system (TCS) based on slip ratio [4], electric stability program (ESP) based on measured rolling angle speed, acceleration, and yaw moment of a vehicle [5]. An accurate vehicle driving state is a precondition for these systems
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