Adaptive EKF-Based Vehicle State Estimation With Online Assessment of Local Observability

Katriniok, Alexander; Abel, Dirk

doi:10.1109/tcst.2015.2488597

Cited by 96 publications

(30 citation statements)

References 31 publications

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“…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%

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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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Section: Observer-based Estimationmentioning

confidence: 99%

Section: Vehicle Modelsmentioning

confidence: 99%

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

2018

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“…where C s and C a are the longitudinal and lateral stiffnesses of the tire, respectively; S x and S y are the longitudinal slip ratio and lateral slip ratio, respectively; m is the coefficient of road adhesion; and F zw is the vertical load of the tire. The longitudinal slip ratio of tire S x can be calculated according to equation (10)…”

Section: Tire Dynamicsmentioning

confidence: 99%

Estimation of states and parameters of multi-axle distributed electric vehicle based on dual unscented Kalman filter

et al. 2019

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Distributed electric drive technology has become an important trend because of its ability to enhance the dynamic performance of multi-axle heavy vehicle. This article presents a joint estimation of vehicle’s state and parameters based on the dual unscented Kalman filter. First, a 12-degrees-of-freedom dynamic model of an 8 × 8 distributed electric vehicle is established. Considering the dynamic variation of some key parameters for heavy vehicle, a real-time parameter estimator is introduced, based on which simultaneous estimation of vehicle’s state and parameters is implemented under the dual unscented Kalman filter framework. Simulation results show that the dual unscented Kalman filter estimator has a high estimation accuracy for multi-axle distributed electric vehicle’s state and key parameters. Therefore, it is reliable for vehicle dynamics control without the influence of unknown or varying parameters.

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“…Note that the decision-making mechanism requires reliable information about surrounding environment and vehicle dynamic states. 3 Active safety systems can benefit significantly from a prior knowledge of the road conditions. In the vehicle dynamics control, the tyre–road friction coefficient and body’s sideslip angle are two important parameters reflecting the adhesion limit and stability of vehicles, respectively.…”

Section: Introductionmentioning

confidence: 99%

Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories

Ping

Cheng

Yue³

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Vehicle dynamic states and parameters, such as the tyre–road friction coefficient and body’s sideslip angle especially, are crucial for vehicle dynamics control with close-loop feedback laws. Autonomous vehicles also have strict demands on real-time knowledge of those information to make reliable decisions. With consideration of the cost saving, some estimation methods employing high-resolution vision and position devices are not for the production vehicles. Meanwhile, the bad adaptability of traditional Kalman filters to variable system structure restricts their practical applications. This paper introduces a cost-efficient estimation scheme using on-board sensors. Improved Strong Tracking Unscented Kalman filter is constructed to estimate the friction coefficient with fast convergence rate on time-variant road surfaces. On the basis of previous step, an estimator based on interactive multiple model is built to tolerant biased noise covariance matrices and observe body’s sideslip angle. After the vehicle modelling errors are considered, a Self-Correction Data Fusion algorithm is developed to integrate results of the estimator and direct integral method with error correction theory. Some simulations and experiments are also implemented, and their results verify the high accuracy and good robustness of the cooperative estimation scheme.

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Adaptive EKF-Based Vehicle State Estimation With Online Assessment of Local Observability

Cited by 96 publications

References 31 publications

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

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

Estimation of states and parameters of multi-axle distributed electric vehicle based on dual unscented Kalman filter

Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories

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