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. 2021 Aug 10;21(16):5409.
doi: 10.3390/s21165409.

A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation

Antonio Leanza  1 , Giulio Reina  1 , José-Luis Blanco-Claraco  2
Affiliations

A Factor-Graph-Based Approach to Vehicle Sideslip Angle Estimation

Antonio Leanza et al. Sensors (Basel). .

Abstract

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.

Keywords: Kalman filtering; factor graph; graphical models; sideslip angle estimation; vehicle dynamics estimation.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Single-track model.
Figure 2
Figure 2
Factor graph (FG) relative to the linear bicycle model.
Figure 3
Figure 3
FG relative to the first three steps of the estimation problem: the first step with priors is shown in the black dashed window, and the generic step is shown in the gray dashed-dotted window, with the involved factors.
Figure 4
Figure 4
Vehicle sideslip angle β estimate at the top and yaw rate r at the bottom obtained by using a KF-based observer applied to the linear bicycle model.
Figure 5
Figure 5
Vehicle sideslip angle β estimate at the top and yaw rate r at the bottom obtained by using FG applied to the linear bicycle model.
Figure 6
Figure 6
Estimation of vehicle sideslip angle β by considering both KF and FG estimators with a window of 5 samples.
Figure 7
Figure 7
Estimation of vehicle sideslip angle β by considering both KF and FG estimators for small values of the sideslip angle.
Figure 8
Figure 8
Estimation of vehicle sideslip angle β by considering both KF and FG estimators with a window of 5 samples, for another set of real data.
Figure 9
Figure 9
Estimation of vehicle sideslip angle β by considering both KF and FG batch estimators.
Figure 10
Figure 10
Absolute error in the estimation of sideslip angle β by considering KF against the FG batch estimator.
Figure 11
Figure 11
Path followed by the vehicle during a single lap: black is ground truth, red represents KF, and green indicates FG.
See this image and copyright information in PMC

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