Abstract
Abstract. It is necessary to acquire the accurate information of vehicle driving states for the implementation of automobile active safety control. To this end, this paper proposes an effective co-estimation method based on an unscented Kalman filter (UKF) algorithm to accurately predict the sideslip angle, yaw rate, and longitudinal speed of a ground vehicle. First, a 3 degrees-of-freedom (DOFs) nonlinear vehicle dynamics model is established as the nominal control plant. Then, based on CarSim software, the simulation results of the front steer angle and longitudinal and lateral acceleration are obtained under a variety of working conditions, which are regarded as the pseudo-measured values. Finally, the joint simulation of vehicle state estimation is realized in the MATLAB/Simulink environment by using the pseudo-measured values and UKF algorithm concurrently. The results show that the proposed UKF-based vehicle driving state estimation method is effective and more accurate in different working scenarios compared with the EKF-based estimation method.
Highlights
As we all know, a variety of vehicles has become a popular and common device in people’s daily lives; an active safety system (ASC) of a ground vehicle plays an important role in avoiding traffic accidents as a means of guaranteeing passenger safety
We proposed a type of unscented Kalman filter (UKF)-based vehicle driving state estimation method with higher accuracy
A 3-DOFs vehicle dynamics model is established, and a vehicle driving state estimation method is designed based on the UKF algorithm
Summary
A variety of vehicles has become a popular and common device in people’s daily lives; an active safety system (ASC) of a ground vehicle plays an important role in avoiding traffic accidents as a means of guaranteeing passenger safety. Like the study above, Liu et al (2016) proposed a state estimation method for four-wheel drive vehicles based on the minimum model error (MME) criterion by combing with the EKF algorithm. Heidfeld et al (2019) proposed a state estimation method for all-wheel drive electric vehicles based on the UKF algorithm, which realized the comprehensive estimation of longitudinal and lateral speed, tire slip angle, and tire friction coefficient on each wheel. The results showed that the joint observer could effectively estimate and identify the relevant vehicle states and parameters and had a good convergence effect Inspired by these studies, in this paper, an effective estimation method is conducted based on the UKF algorithm in order to accurately estimate the driving states of vehicles. Where the state matrices A, B, C, and D are described in Appendix A
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