mlsanityData to 14 Oct 2026

Damper-B-PINN: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Vehicle State Estimation

arXiv 2502.20772 ↗First posted 28 Feb 2025cs.AI cs.LGcited by 1

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Abstract (arXiv)

Accurate state estimation is fundamental to intelligent vehicles. Wheel load, one of the most important chassis states, serves as an essential input for advanced driver assistance systems (ADAS) and exerts a direct influence on vehicle stability and safety. However, wheel load estimation remains challenging due to the complexity of chassis modeling and the susceptibility of nonlinear systems to noise. To address these issues, this paper first introduces a refined suspension linkage-level modeling approach that constructs a nonlinear instantaneous dynamic model by explicitly considering the complex geometric structure of the suspension. Building upon this, we propose a damper characteristics-based Bayesian physics-informed neural network (Damper-B-PINN) framework to estimate dynamic wheel load, which leverages the suspension dynamics as physical guidance of PINN while employing Bayesian inference to mitigate the effects of system noise and uncertainty. Moreover, a damper-characteristic physics conditioning (DPC) module is designed for embedding physical prior. The proposed Damper-B-PINN is evaluated using both high-fidelity simulation datasets generated by CarSim software and real-world datasets collected from a Formula Student race car. Experimental results demonstrate that our Damper-B-PINN consistently outperforms existing methods across various test conditions, particularly extreme ones. These findings highlight the potential of the proposed Damper-B-PINN framework to enhance the accuracy and robustness of dynamic wheel load estimation, thereby improving the reliability and safety of ADAS applications.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 66 references that are in the map. First posted on arXiv 28 Feb 2025.

Builds on

27 of 66 references.

References · 27

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  3. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,333
  4. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  5. 2022Temporal Difference Learning for Model Predictive Controlcited by 397
  6. 2020B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Datacited by 335
  7. 2017Concrete Dropoutcited by 227
  8. 2020Physics-informed learning of governing equations from scarce datacited by 167
  9. 2015A Theoretically Grounded Application of Dropout in Recurrent Neural Networkscited by 161
  10. 2018Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problemscited by 147
  11. 2024Challenges in Training PINNs: A Loss Landscape Perspectivecited by 113
  12. 2019'In-Between' Uncertainty in Bayesian Neural Networkscited by 80
15 more
  1. 2019Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networkscited by 76
  2. 2019Quality of Uncertainty Quantification for Bayesian Neural Network Inferencecited by 59
  3. 2020Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imagingcited by 40
  4. 2020nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applicationscited by 36
  5. 2023Correcting model misspecification in physics-informed neural networks (PINNs)cited by 33
  6. 2024Learning Physics Informed Neural ODEs With Partial Measurementscited by 9
  7. 2020Adaptive Physics-Informed Neural Networks for Markov-Chain Monte Carlocited by 6
  8. 2023Efficient Bayesian inference using physics-informed invertible neural networks for inverse problemscited by 6
  9. 2025Deep Operator Networks for Bayesian Parameter Estimation in PDEscited by 6
  10. 2022Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networkscited by 4
  11. 2024Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systemscited by 4
  12. 2024Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networkscited by 4
  13. 2024EV-PINN: A Physics-Informed Neural Network for Predicting Electric Vehicle Dynamicscited by 4
  14. 2024On the estimation rate of Bayesian PINN for inverse problemscited by 3
  15. 2016A Bayesian Approach for Parameter Estimation with Uncertainty for Dynamic Power Systemscited by 2

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  1. 2026VehDyn: A Driving World Model Benchmark for Vehicle Dynamicscited by 0