Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network

Chindamo, Daniel; Gadola, Marco

doi:10.1051/matecconf/201816602001

Cited by 32 publications

(16 citation statements)

References 19 publications

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“…Moreover, with the use of a lateral G sensor signal and by including road bank angle effect, the front and rear cornering stiffness and vehicle sideslip angle are identified, in the absence of any a priori knowledge of the road bank angle. In order to completely overcome the need for a vehicle model of any kind and its related complex set of parameters, different approaches based on artificial neural networks (ANNs) [ 24 , 25 , 26 , 27 ] are now widely investigated, since they are suitable for modeling complex systems using their ability to identify relationships from input–output data.…”

Section: Introductionmentioning

confidence: 99%

A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation

Leanza

Reina

Blanco-Claraco

2021

Sensors

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Sideslip angle is an important variable for understanding and monitoring vehicle dynamics, but there is currently no inexpensive method for its direct measurement. Therefore, it is typically estimated from proprioceptive sensors onboard using filtering methods from the family of the Kalman filter. As a novel alternative, this work proposes modeling the problem directly as a graphical model (factor graph), which can then be optimized using a variety of methods, such as whole-dataset batch optimization for offline processing or fixed-lag smoothing for on-line operation. Experimental results on real vehicle datasets validate the proposal, demonstrating a good agreement between estimated and actual sideslip angle, showing similar performance to state-of-the-art methods but with a greater potential for future extensions due to the more flexible mathematical framework. An open-source implementation of the proposed framework has been made available online.

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Section: Introductionmentioning

confidence: 99%

A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation

Leanza

Reina

Blanco-Claraco

2021

Sensors

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“…The results show that the NN‐based estimation algorithm can yield good performance at high vehicle velocities. Similarly, Chindamo et al put forward a succinct approach to obtain the training datasets for NN model training in specific manoeuvres [91]. Torben Gräber et al [92] presented a supervised machine learning scheme that consists of a recurrent NN with gated recurrent units, an additional input projection and a regression head.…”

Section: Overview Of Sideslip Angle Estimation Methodsmentioning

confidence: 99%

Sideslip angle estimation of ground vehicles: a comparative study

Liu

Wang

Walker

2020

IET Control Theory & Appl

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Vehicle sideslip angle is a major indicator of dynamics stability for ground vehicles; but it is immeasurable with commercially‐available sensors. Sideslip angle estimation has been the focus of intensive research in past decades, resulting in a rich library of related literature. This study presents a comprehensive evaluation of state‐of‐the‐art sideslip angle estimation methods, with the primary goal of quantitatively revealing their strengths and limitations. These include kinematics‐, dynamics‐ and neural network‐based estimators. A hardware‐in‐loop system is purposely established to examine their performance under four typical manoeuvres. The results show that the dynamics‐based estimators are suitable at low vehicle velocities when tires operate in the linear region. In contrast, the kinematics‐based methods yield superior estimation performance at high vehicle velocities, and the inclusion of the dual GPS receivers is beneficial even when there is large disturbance to the steering angle. Of utmost importance, it is experimentally manifested that the neural network‐based estimator can perform well in all manoeuvres once the training datasets are properly selected.

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“…The results show that the estimation algorithm based on the neural network demonstrates good performance under high-speed conditions. Chindamo et al [15] proposed a concise method to obtain training data under specific working conditions. Torben et al [16] proposed a scheme based on the recurrent neural network.…”

Section: Neural Network 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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Estimation of Vehicle Side-Slip Angle Using an Artificial Neural Network

Cited by 32 publications

References 19 publications

A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation

A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation

Sideslip angle estimation of ground vehicles: a comparative study

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

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