Vehicle Lateral and Longitudinal Velocity Estimation Using Coupled EKF and RLS Methods

Liang, Chao; Zhang, Yong Sheng; Shi, Yan; Xu, Ming; Ou, Yang

doi:10.4028/www.scientific.net/amm.29-32.851

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The total steering angle TS (input 4) is computed as the sum of the steering wheel angle (defined as the angle between technique. Further approaches exploit the Kalman Filter (KF) [15], Adaptive Kalman Filter (AKF) [16] and its nonlinear versions, Extended Kalman Filter (EKF) [17] and Unscented Kalman Filter (UKF) [18]. Other methods rely on similar filter/observer-based techniques [19][20].…”

Section: Estimation Methods and Vehicle Setupmentioning

confidence: 99%

A Data-Driven Method for Vehicle Speed Estimation

Bonfitto¹,

Feraco²

2021

Komunikácie

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This paper presents a method based on Artificial Neural Networks for estimation of the vehicle speed. The technique exploits the combination of two tasks: a) speed estimation by means of regression neural networks dedicated to different road conditions (dry, wet and icy); b) identification of the road condition with a pattern recognition neural network. The training of the networks is conducted with experimental datasets recorded during the driving sessions performed with a vehicle on different tracks. The effectiveness of the proposed approach is validated experimentally on the same car by deploying the algorithm on a dSPACE computing platform. The estimation accuracy is evaluated by comparing the obtained results to the measurement of an optical sensor installed on the vehicle and to the output of another estimation method, based on the mean value of velocity of the four wheels.

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Section: Estimation Methods and Vehicle Setupmentioning

confidence: 99%

A Data-Driven Method for Vehicle Speed Estimation

Bonfitto¹,

Feraco²

2021

Komunikácie

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“…However, this simple approach may be strongly inaccurate when one or more wheels are locking or skidding during the extreme maneuvers, which is a common condition for racing vehicles and in the case of dirty asphalt, as well as icy or wet roads. On the other hand, model-based and filter/observer-based methods have been also investigated [7], such as nonlinear observers [8], Kalman Filter (KF) [9][10], Adaptive KF (AKF) [11][12], Extended KF (EKF) [13][14], and Unscented Kalman Filter (UKF) [15][16]. Although effective, these methods may suffer severe inaccuracies due to unmodeled dynamics or when the reference model is not tuned to represent all the driving conditions and possible vehicle setups and tuning, which can be a frequent situation when dealing with the high-performance or racing vehicles.…”

Section: Issn 1335-4205 (Print Version) Issn 2585-7878 (Online Version)mentioning

confidence: 99%

Fuzzy Logic Method for the Speed Estimation in All-Wheel Drive Electric Racing Vehicles

Bonfitto¹,

Feraco²,

Rossini³

et al. 2021

Komunikácie

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This paper presents a method for the vehicle speed estimation with a Fuzzy Logic based algorithm. The algorithm acquires the measurements of the yaw rate, steering angle, wheel velocities and exploits a set of five Fuzzy Logics dedicated to different driving conditions. The technique estimates the speed exploiting a weighted average of the contributions provided by the longitudinal acceleration and the credibility assigned by the Fuzzy Logics to the measurements of the wheels’ speed. The method is experimentally evaluated on an all-wheel drive electric racing vehicle and is valid for the front and rear wheel drive configurations. The experimental validation is performed by comparing the obtained estimation with the result of computing the speed as the average of the linear velocity of the four wheels. A comparison to the integral of the vehicle acceleration over time is reported.

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“…However, in case of the lateral vehicle velocity, side slip angle, and roll angle, it is practically impossible to measure the values due to sensor cost problems. For this reason, a variety of researches on these states estimation have been carried out [6]- [10]. In [6], lateral vehicle velocity estimation algorithm was proposed using vehicle model, RLS algorithm, and Extended Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, a variety of researches on these states estimation have been carried out [6]- [10]. In [6], lateral vehicle velocity estimation algorithm was proposed using vehicle model, RLS algorithm, and Extended Kalman filter. In conventional methods (e.g., vehicle model-based method and sensor kinematics-based method), there are challenging issues such as how to compensate vehicle model uncertainties and remove numerical errors K [8].…”

Section: Introductionmentioning

confidence: 99%

Vehicle state estimation for advanced vehicle motion control using novel lateral tire force sensors

Nam

Fujimoto

et al. 2011

Proceedings of the 2011 American Control Conference

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In this paper, new real-time methods for the lateral vehicle velocity and roll angle estimation are presented. Lateral tire forces, obtained from a multi-sensing hub (MSHub) unit, are used to estimate lateral vehicle velocity and a roll angle. In order to estimate lateral vehicle velocity, the recursive least square (RLS) algorithm is utilized based on a linear vehicle model and sensor measurements. In the roll angle estimation, the Kalman filter is designed for real-time estimation. The proposed estimation methods, RLS-based estimator and the Kalman filter, were verified by field tests on an experimental electric vehicle. Test results show that the proposed estimation methods provide better estimation performances and these methods are robust to road conditions. 978-1-4577-

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Vehicle Lateral and Longitudinal Velocity Estimation Using Coupled EKF and RLS Methods

Cited by 3 publications

References 8 publications

A Data-Driven Method for Vehicle Speed Estimation

A Data-Driven Method for Vehicle Speed Estimation

Fuzzy Logic Method for the Speed Estimation in All-Wheel Drive Electric Racing Vehicles

Vehicle state estimation for advanced vehicle motion control using novel lateral tire force sensors

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