Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics

Abstract

Automotive research and development passed through a vast evolution during past decades. Many passive and active driver assistance systems were developed, increasing the passengers' safety and comfort. This ongoing process is a main focus in current research and offers great potential for further systems, especially focusing on the task of autonomous and cooperative driving in the future. For that reason, information about the current stability in terms of dynamic behavior and vehicle environment are necessary for the systems to perform properly. Thus, model-based online state and parameter estimation have become important throughout the last years using a detailed vehicle model and standard sensors, gathering this information. In this chapter, state and parameter estimation in vehicle dynamics utilizing the unscented Kalman filter is presented. The estimation runs in real time based on a detailed vehicle model and standard measurements taken within the car. The results are validated using a Volkswagen Golf GTE Plug-In Hybrid for various dynamic test maneuvers and a Genesys Automotive Dynamic Motion Analyzer (ADMA) measurement unit for highprecision measurements of the vehicle's states. Online parameter estimation is shown for friction coefficient estimation performing maneuvers on different road surfaces.

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

“…This issue proves that the KF suits real-time application systems with a minimum system specification requirement [91]. However, KF is limited to linear models of domain problems [92].…”

On the vehicle state estimation benefits of smart tires

“…The state and input vectors resemble those of the UKF described in [2], which was implemented on a real car by Bosch. The output vector includes the variables that are available on the controller area network (CAN) bus of the vehicle, see [3], [4]. Moreover, due to the availability of the estimated longitudinal tire forces, %,01 , and vertical loads, ",01 , output by the Cyber TM Tyre system, the vector is augmented accordingly.…”

“…The measurement noise covariance , i.e., the variance of the sensors, was determined through the a-priori analysis of sample measurements [14], whilst the matrix is a tuning parameter that takes model uncertainties into account. To reduce the number of tuning parameters, the initial error system covariance matrix is set to be equal to , as in [4]. The additional parameters are -‹ , a constant defining the spread of the sigma points, which is set to 1 in the proposed implementations; -‹ , a constant related to the type of probability distribution, equal to 2 for Gaussian distribution; and -‹ , a scaling parameter, set to 1 [1].…”

“…Despite the good accuracy provided by Extended Kalman filters, nowadays Unscented Kalman filters (UKFs) are widely adopted for their superior robustness with respect to sampling rates and approximation errors, for the same computational effort [1]. UKFs have also been proven to be effective in estimating vehicle parameters, in addition to vehicle motion states, see [2]- [4].…”

Vehicle state estimation based on extended Kalman filter and radial basis function neural networks

“…The commonly used vehicle state parameter estimation methods include Kalman filter (KF) and its improved algorithms, [7][8][9][10][11][12][13][14][15][16][17] neural network estimation algorithms, [18][19][20] and other related estimation algorithms. [21][22][23][24][25][26][27] However, single estimation algorithms have their own limitations, such as the uncertainty of mathematical model parameters, noise parameters, and the coverage of training samples, which will affect the estimation results and may lead to the sudden divergence of estimation accuracy.…”