Estimating friction coefficient using generative modelling

• URL: http://arxiv.org/abs/2303.05927v1
• Date: Fri, 10 Mar 2023 13:58:28 GMT
• Title: Estimating friction coefficient using generative modelling
• Abstract summary: This work aims to formulate the problem of friction estimation as a visual perceptual learning task. The problem is broken down into detecting surface characteristics by applying semantic segmentation and using the extracted features to predict the frictional force.
• Score: 0.7503129292751939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is common to utilise dynamic models to measure the tyre-road friction in real-time. Alternatively, predictive approaches estimate the tyre-road friction by identifying the environmental factors affecting it. This work aims to formulate the problem of friction estimation as a visual perceptual learning task. The problem is broken down into detecting surface characteristics by applying semantic segmentation and using the extracted features to predict the frictional force. This work for the first time formulates the friction estimation problem as a regression from the latent space of a semantic segmentation model. The preliminary results indicate that this approach can estimate frictional force.

Related papers

• Smoothness-Based Derandomization of PAC-Bayes Bounds [48.06655334157014]
  We study PAC-Bayes derandomization for smooth loss functions.<n>We show that unbounded passing from the Gibbs predictor to the deterministic predictor at the posterior mean has a precise cost.<n>We specialize the results to linear predictors and smooth neural networks.
  arXiv  Detail & Related papers  (2026-06-17T14:17:44Z)
• Non-Parametric Rehearsal Learning via Conditional Mean Embeddings [88.89267783967263]
  We propose a non-parametric rehearsal learning approach for textitavoiding undesired future (AUF) problems.<n>Specifically, we use kernel machinery to reformulate the AUF objective into a unified representation that disentangles desirability modeling from action-induced distributional changes.
  arXiv  Detail & Related papers  (2026-05-09T15:30:52Z)
• Neural Quasiprobabilistic Likelihood Ratio Estimation with Negatively Weighted Data [2.5923147161639104]
  We consider a generalisation of the likelihood-ratio estimation task to a quasiprobabilistic setting where probability densities can be negative. The presence of negative densities and negative weights, pose an array of challenges to traditional neural likelihood ratio estimation methods.
  arXiv  Detail & Related papers  (2024-10-14T07:15:49Z)
• Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Models [44.56781176879151]
  State-of-the-art Natural Language Inference (NLI) models are sensitive towards minor semantics preserving surface-form variations. We show that semantic sensitivity causes performance degradations of $12.92%$ and $23.71%$ average over $textbfin-$ and $textbfout-of-$ domain settings.
  arXiv  Detail & Related papers  (2024-01-25T14:47:05Z)
• Geo-Localization Based on Dynamically Weighted Factor-Graph [74.75763142610717]
  Feature-based geo-localization relies on associating features extracted from aerial imagery with those detected by the vehicle's sensors. This requires that the type of landmarks must be observable from both sources. We present a dynamically weighted factor graph model for the vehicle's trajectory estimation.
  arXiv  Detail & Related papers  (2023-11-13T12:44:14Z)
• Lightweight Regression Model with Prediction Interval Estimation for Computer Vision-based Winter Road Surface Condition Monitoring [0.4972323953932129]
  This paper proposes a deep learning regression model, SIWNet, capable of estimating road surface friction properties from camera images. SIWNet extends state of the art by including an uncertainty estimation mechanism in the architecture. The model was trained and tested with the SeeingThroughFog dataset, which features corresponding road friction sensor readings and images from an instrumented vehicle.
  arXiv  Detail & Related papers  (2023-10-02T06:33:06Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system [0.0]
  Online estimation of the road-friction coefficient is an essential feature for any advanced brake control system. In this study, a data-driven scheme based on a Neural Net is proposed to estimate the optimum friction coefficient as a function of windowed slip-friction measurements. Open loop and closed loop simulations of the landing phase of an aircraft on an unknown surface are used to show the potentiality and efficacy of the proposed robust friction estimation approach.
  arXiv  Detail & Related papers  (2022-11-14T10:36:14Z)
• Interpretable Social Anchors for Human Trajectory Forecasting in Crowds [84.20437268671733]
  We propose a neural network-based system to predict human trajectory in crowds. We learn interpretable rule-based intents, and then utilise the expressibility of neural networks to model scene-specific residual. Our architecture is tested on the interaction-centric benchmark TrajNet++.
  arXiv  Detail & Related papers  (2021-05-07T09:22:34Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction [75.1213178617367]
  We propose a hybrid approach combining deep learning and physical motion models. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
  arXiv  Detail & Related papers  (2021-03-11T15:21:08Z)
• Probabilistic Surface Friction Estimation Based on Visual and Haptic Measurements [17.477520575909193]
  We propose a joint visuo-haptic object model that enables the estimation of surface friction coefficient over an entire object. We demonstrate the validity of the proposed method by showing its ability to estimate varying friction coefficients on a range of real multi-material objects.
  arXiv  Detail & Related papers  (2020-10-16T09:58:39Z)
• Emulator-based global sensitivity analysis for flow-like landslide run-out models [0.0]
  Landslide run-out modeling involves uncertainties originating from model input data. A global sensitivity analysis that is capable of exploring the entire input space and accounts for all interactions, often remains limited due to computational challenges. We address this research gap by integrating Gaussian process emulation into landslide run-out modeling and apply it to the open-source simulation tool r.avaflow.
  arXiv  Detail & Related papers  (2020-10-08T15:21:52Z)
• Composing Normalizing Flows for Inverse Problems [89.06155049265641]
  We propose a framework for approximate inference that estimates the target conditional as a composition of two flow models. Our method is evaluated on a variety of inverse problems and is shown to produce high-quality samples with uncertainty.
  arXiv  Detail & Related papers  (2020-02-26T19:01:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.