Title: Research on vehicle state estimation based on robust adaptive unscented particle filter

Authors: Yingjie Liu; Dawei Cui; Yalin Wang

Addresses: School of Machinery and Automation, Weifang University, Weifang, Shandong, China ' School of Machinery and Automation, Weifang University, Weifang, Shandong, China ' School of Machinery and Automation, Weifang University, Weifang, Shandong, China

Abstract: In order to reduce the influence of historical measurement data errors, a filter estimation method of vehicle state named Robust Adaptive Unscented Particle Filter (RAUPF) is proposed. Firstly, a 3-DOF non-linear vehicle dynamics model was established. Then, a joint simulation platform was established. At the same time, the simulation was conducted under three different operating conditions: the sine delay test and the double lane change test and the slop input test. The results showed that compared to the Unscented Particle Filter (UPF) algorithm, the Root Mean Square Error (RMSE) and average absolute error (MAE) of the estimation value of the RAUPF are smaller. And also, compared to the UPF algorithm, the robustness of the RAUPF method is better. The proposed RAUPF algorithm can effectively suppress noise fluctuations and improve estimation accuracy.

Keywords: vehicle engineering; state estimation; RAUPF.

DOI: 10.1504/IJVS.2023.137655

International Journal of Vehicle Safety, 2023 Vol.13 No.1, pp.1 - 18

Received: 12 Apr 2022
Received in revised form: 16 May 2023
Accepted: 07 Jul 2023
Published online: 02 Apr 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article