Robust vehicle state and tire force estimation: Highlights on effects of road angles and sensor performance

Vaseur, Cyrano; Aalst, Sebastiaan van; Desmet, Wim

doi:10.1109/iv48863.2021.9575640

Cited by 1 publication

(1 citation statement)

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“…Fig. 10b shows the vehicle's lateral and longitudinal acceleration, and it highlights how the driver is pushing the vehicle at the limit of handling in all the corners, see [1,7] s, [16,19] s and [23,29] s. The sideslip angle estimation performance of the four approaches is represented in Fig. 10a.…”

Section: A Full Dataset Resultsmentioning

confidence: 99%

An Unscented Kalman Filter-Informed Neural Network for Vehicle Sideslip Angle Estimation

Bertipaglia,

Alirezaei,

Happee

et al. 2024

IEEE Trans. Veh. Technol.

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This paper proposes a novel vehicle sideslip angle estimator, which uses the physical knowledge from an Unscented Kalman Filter (UKF) based on a non-linear single-track vehicle model to enhance the estimation accuracy of a Convolutional Neural Network (CNN). The model-based and data-driven approaches interact mutually, and both use the standard inertial measurement unit and the tyre forces measured by load sensing technology. CNN benefits from the UKF the capacity to leverage the laws of physics. Concurrently, the UKF uses the CNN outputs as sideslip angle pseudo-measurement and adaptive process noise parameters. The back-propagation through time algorithm is applied end-to-end to the CNN and the UKF to employ the mutualistic property. Using a large-scale experimental dataset of 216 manoeuvres containing a great diversity of vehicle behaviours, we demonstrate a significant improvement in the accuracy of the proposed architecture over the current state-of-art hybrid approach combined with model-based and data-driven techniques. In the case that a limited dataset is provided for the training phase, the proposed hybrid approach still guarantees estimation robustness.

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