A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches

Li, Xiaoyü; Xu, Nan; Li, Qin; Guo, Konghui; Zhou, Jianfeng

doi:10.1177/0954407019892156

Cited by 20 publications

(7 citation statements)

References 22 publications

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“…Li et al [11] proposed a fusion estimation method composed of a kinematics estimator and a motion geometry estimator. The kinematics estimator uses an extended Kalman filter (EKF) based on a 3-DOF (degree of freedom) single-track vehicle model as an observer.…”

Section: Model Based Approachmentioning

confidence: 99%

Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network

Kong

Wen²,

Zhao³

et al. 2021

WEVJ

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Lateral velocity is an important parameter to characterize vehicle stability. The acquisition of lateral velocity is of great significance to vehicle stability control and the trajectory following control of autonomous vehicles. Aiming to resolve the problems of poor estimation accuracy caused by the insufficient modeling of traditional model-based methods and significant decline in performance in the case of a change in road friction coefficient, a deep learning method for lateral velocity estimation using an LSTM, long-term and short-term memory network, is designed. LSTM can well reflect the inertial characteristics of vehicles. The training data set contains sensor data under various working conditions and roads. The simulation results show that the prediction model has high accuracy in general and robustness to the change of road friction coefficient.

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Section: Model Based Approachmentioning

confidence: 99%

Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network

Kong

Wen²,

Zhao³

et al. 2021

WEVJ

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“…Therefore, in order to reduce production and design costs, it is necessary to design a reliable vehicle driving state estimation method to replace physical sensors by soft measurement. 1…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in order to reduce production and design costs, it is necessary to design a reliable vehicle driving state estimation method to replace physical sensors by soft measurement. 1 At present, there have been many studies on the estimation method of vehicle driving state, among which, the model-based vehicle driving state estimator is the most common method. Researchers usually design a vehicle state estimator based on a recognized vehicle model and use the corresponding estimation algorithm to design a vehicle state estimator.…”

Section: Introductionmentioning

confidence: 99%

Vehicle dynamics prediction via adaptive robust unscented particle filter

Liu

Cui

2023

Advances in Mechanical Engineering

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Accurate knowledge of the vehicle dynamics response is a critical aspect to improve handling performance while ensuring safe driving at the same time. However, it poses a challenge since not all the quantities of interest can be directly measured due to cost and/or technological reasons. Therefore, combining the principle of robust filtering and unscented particle filtering algorithm, a filter estimation method of vehicle state is proposed to estimate driving state parameters of a vehicle. The adaptive robust unscented particle filter (ARUPF) is used to realize the longitudinal and lateral velocity as well as the side slip angle of the vehicle. The CarSim and Matlab/Simulink co-simulation platform is established to verify the estimation algorithm. The results show that based on the adaptive robust unscented particle filter algorithm, the vehicle driving states can be estimated, the measurement parameters can be effectively filtered, and the estimation accuracy is higher.

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“…The previous works focus on adding additional heading information to directly correct the attitude error. [20][21][22] The heading angle from the dual-antenna GNSS system is aligned to the vehicle body frame with a regression method and the method achieves improved results. 20 The dualantenna heading angle is naturally calculated with the phase position of satellite signals and provides an absolute and direct measurement for the vehicle attitude.…”

Section: Introductionmentioning

confidence: 99%

Precise and robust sideslip angle estimation based on INS/GNSS integration using invariant extended Kalman filter

Zhang

Zhao

Huang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Sideslip angle estimation is vital to the safety and active control of autonomous vehicles. In this paper, an innovative vehicle kinematic-based sideslip angle estimation method is proposed. The method is built on multi-sensor fusion, which fuses the information of inertial measurement unit, global navigation satellite system (GNSS), and onboard sensors to continuously estimate the attitude and velocity of the vehicle and thus obtain the sideslip angle. The invariant extended Kalman filter framework is adopted and the left-invariant form is derived to implement the GNSS measurement update. In order to solve the unobservability problem when only a single GNSS receiver is equipped, the vehicle motion constraint is introduced into the filter to improve the accuracy of heading angle estimation. The method is validated by field test and the results show that the method is robust in terms of converge efficiency. Furthermore, the sideslip angle estimation accuracy is satisfactory with the average absolute error less than 0.25°, which meets the active safety control requirements for autonomous driving.

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A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches

Cited by 20 publications

References 22 publications

Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network

Vehicle Lateral Velocity Estimation Based on Long Short-Term Memory Network

Vehicle dynamics prediction via adaptive robust unscented particle filter

Precise and robust sideslip angle estimation based on INS/GNSS integration using invariant extended Kalman filter

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