mlsanityData to 14 Oct 2026

DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation

arXiv 2605.24860 ↗First posted 24 May 2026eess.SY cs.AI cs.ETcited by 1

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Abstract (arXiv)

Advanced driver assistance systems (ADAS) play an important role in modern automotive intelligence, significantly enhancing vehicle safety and stability. The performance of ADAS critically relies on accurate and reliable vehicle state estimation, particularly from vehicle dynamic sensors. Among these signals, wheel load is a key variable for chassis control and safety-critical functions, yet it remains difficult to estimate robustly due to complex suspension geometry, nonlinear dynamics, and measurement noise. To address this issue, we propose DBPnet, a Bayesian physics-informed neural network (PINN) with a physics-aware embedding module inspired by damper characteristics. First, this paper presents a suspension linkage-level modeling (SLLM) approach that constructs a nonlinear instantaneous dynamic model by explicitly considering the complex geometric structure of the suspension. Building upon SLLM, Bayesian inference is integrated into the PINN to effectively cope with noise and uncertainty in the vehicle chassis system, thereby improving the model's robustness. Then, a physics-informed loss function is employed to ensure consistency with fundamental physical principles, while the damper characteristics-inspired embedding module extracts temporal variation features of input signals and incorporates them into each layer of the PINN, ensuring that physical observations guide the neural network without being constrained by fixed physical models. Extensive evaluations on high-fidelity simulations and real-world experiments demonstrate that our DBPnet consistently achieves lower RMSE and MaxError than baseline methods. These results highlight the potential of our DBPnet to advance wheel load estimation and contribute to the development of more reliable ADAS actuator functions.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 25 of its 57 references that are in the map. First posted on arXiv 24 May 2026.

Builds on

25 of 57 references.

References · 25

  1. 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,333
  2. 2020B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Datacited by 335
  3. 2015A Theoretically Grounded Application of Dropout in Recurrent Neural Networkscited by 161
  4. 2018Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problemscited by 147
  5. 2021KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamicscited by 132
  6. 2024Challenges in Training PINNs: A Loss Landscape Perspectivecited by 113
  7. 2019Quality of Uncertainty Quantification for Bayesian Neural Network Inferencecited by 59
  8. 2022Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systemscited by 54
  9. 2021Physics-Guided Deep Learning for Dynamical Systems: A Surveycited by 44
  10. 2020nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applicationscited by 36
  11. 2017The Ensemble Kalman Filter: A Signal Processing Perspectivecited by 17
  12. 2023Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signalscited by 17
13 more
  1. 2024KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filtercited by 11
  2. 2023Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filtercited by 10
  3. 2023GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filteringcited by 10
  4. 2020Adaptive Physics-Informed Neural Networks for Markov-Chain Monte Carlocited by 6
  5. 2023Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversioncited by 6
  6. 2023Efficient Bayesian inference using physics-informed invertible neural networks for inverse problemscited by 6
  7. 2025Deep Operator Networks for Bayesian Parameter Estimation in PDEscited by 6
  8. 2023An Unscented Kalman Filter-Informed Neural Network for Vehicle Sideslip Angle Estimationcited by 5
  9. 2024Bayesian Physics-informed Neural Networks for System Identification of Inverter-dominated Power Systemscited by 4
  10. 2024EV-PINN: A Physics-Informed Neural Network for Predicting Electric Vehicle Dynamicscited by 4
  11. 2024On the estimation rate of Bayesian PINN for inverse problemscited by 3
  12. 2016A Bayesian Approach for Parameter Estimation with Uncertainty for Dynamic Power Systemscited by 2
  13. 2024RKFNet: A Novel Neural Network Aided Robust Kalman Filtercited by 2

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Citing papers per year: 2026: 1.

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