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Comparative evaluation of Kalman filters and motion models in vehicular state estimation and path prediction

Published online by Cambridge University Press:  04 June 2021

Lu Tao ,
Yousuke Watanabe ,
Shunya Yamada  and
Hiroaki Takada
Lu Tao*
Affiliation:
Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, Japan.
Yousuke Watanabe
Affiliation:
Institute of Innovation for Future Society, Nagoya University, Chikusa, Nagoya, Aichi, Japan
Shunya Yamada
Affiliation:
Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, Japan.
Hiroaki Takada
Affiliation:
Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Aichi, Japan. Institute of Innovation for Future Society, Nagoya University, Chikusa, Nagoya, Aichi, Japan
*
Corresponding author. E-mail: tao.lu@a.mbox.nagoya-u.ac.jp
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Abstract

Vehicle state estimation and path prediction, which usually involve Kalman filter and motion model, are critical tasks for intelligent driving. In vehicle state estimation, the comparative performance assessment, regarding accuracy and efficiency, of the unscented Kalman filter (UKF) and the extended Kalman filter (EKF) is rarely discussed. This paper is devoted to empirically evaluating the performance of UKF and EKF incorporating different motion models and investigating the models’ properties and the affecting factors in path prediction. Extensive real world experiments have been carried out and the results show that EKF and UKF have roughly identical accuracy in state estimation; however, EKF is faster than UKF generally; the fastest filter is about 2⋅6 times faster than the slowest. The path prediction experiments reveal that the velocity estimate and the used motion model affect path prediction; the more realistically the model reflects the vehicle's driving status, the more reliable its predictions.

Keywords

estimationextended Kalman filterunscented Kalman filterpath
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 5 , September 2021 , pp. 1142 - 1160
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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