Vehicle side slip angle estimation with stiffness adaptation

Dakhlallah, Jamil; Glaser, Sébastien; Mammar, Saïd

doi:10.1504/ijvas.2010.034099

Cited by 6 publications

(7 citation statements)

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“…On the other hand, four-contact models provide a better description of vehicle dynamics, but obviously at the cost of a greater number of parameters and an increase in the complexity of the systems. For instance, the authors in [ 17 ] use an EKF applied to a four-contact vehicle model with a Dugoff tire model in order to estimate the sideslip angle and the tire/road forces. The authors in [ 18 ] again use a four-contact model, but with a semiphysical nonlinear tire model called “Unitire”, and apply a reduced-order sliding mode observer (SMO), evaluating the performance of the proposed method by means of simulation and experiments.…”

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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“…However, the KF is very simple to be used and implemented; moreover, its robustness, stability, and ability to deal with input and measurement noise make it the most used observer for VSA estimation [18]. This statement is also supported by [25], where after a comparison between the EKF, LO, and SMO concerning the VSA estimation using a non-linear single-track model, EKF achieved a smaller estimation error than the LO and SMO.…”

Section: Kalman Filtermentioning

confidence: 86%

“…Nonetheless, the KF observers are still relatively simple to implement. In summary, the most used observer for VSA estimation is the KF, due to its ability to use input and measurement noise information directly, and because it is robust, stable, and relatively simple to implement [15,18,19].…”

Section: Observer-based Estimationmentioning

confidence: 99%

“…On the one hand, the kinematic model is concerned with the vehicle motion with no reference to forces; thus, it does not need complex parameters such as those regarding tyres. Figure 1 depicts a vehicle model showing: the vehicle velocity at the centre of gravity (G) V G , the longitudinal and lateral components of V G , respectively u and v, the vehicle yaw rate r, the VSA β, the vehicle track t (here assumed to be the same for the front and the rear), the front and rear semi-wheelbases, respectively a 1 and a 2 , and the vehicle wheelbase l. In summary, the most used observer for VSA estimation is the KF, due to its ability to use input and measurement noise information directly, and because it is robust, stable, and relatively simple to implement [15,18,19].…”

Section: Vehicle Modelsmentioning

confidence: 99%

“…Some authors [13,18,31] tried to overcome the problem by using a simple single-track model with cornering stiffness adaption. Results show that VSA estimation is very close to the actual value except for some VSA peaks, where the estimated VSA values deviate from the actual values.…”

Section: Kalman Filtermentioning

confidence: 99%

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On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

2018

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Typical active safety systems that control the dynamics of passenger cars rely on the real-time monitoring of the vehicle sideslip angle (VSA), together with other signals such as the wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, which is known as the yaw rate. The VSA (also known as the attitude or "drifting" angle) is defined as the angle between the vehicle's longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory; therefore, it is a vital piece of information enabling directional stability assessment, such as in transience following emergency manoeuvres, for instance. As explained in the introduction, the VSA is not measured directly for impracticality, and it is estimated on the basis of available measurements such as wheel velocities, linear and angular accelerations, etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e., observer-based and neural network-based, focussing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. The advantages and limitations of each technique have been highlighted and discussed.

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