Adaptive state estimation based on particle filter algorithm for vehicle stability control

Junlong Tao; Yi Wang; Zhe Li; Feihua Huang; Zhichao Zhao; Zihao Gao

doi:10.1117/12.2619775

22 December 2021 Adaptive state estimation based on particle filter algorithm for vehicle stability control

Junlong Tao, Yi Wang, Zhe Li, Feihua Huang, Zhichao Zhao, Zihao Gao

Proceedings Volume 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021); 120585W (2021) https://doi.org/10.1117/12.2619775
Event: 5th International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 2021, Chongqing, China

Abstract

Based on the vehicle plane motion model, a state estimator of vehicle longitudinal velocity and lateral velocity is designed by using particle filter algorithm in this paper. The estimator does not depend on any tire force information, so it avoids the additional estimation error or calculation amount caused by the acquisition of tire force. In view of the change of process noise standard deviation caused by different external factors such as traffic road conditions, this paper uses adaptive standard deviation instead of fixed standard deviation to correct the state transition equation. MATLAB/Simulink and CarSim co-simulation platform were used to verify the proposed particle filter estimator. And the simulation results show that the proposed estimator can provide accurate longitudinal and lateral velocity estimation results for vehicle stability control under various driving conditions.

Citation Download Citation

Junlong Tao, Yi Wang, Zhe Li, Feihua Huang, Zhichao Zhao, and Zihao Gao "Adaptive state estimation based on particle filter algorithm for vehicle stability control", Proc. SPIE 12058, Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021), 120585W (22 December 2021); https://doi.org/10.1117/12.2619775

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