[2303.05927] Estimating friction coefficient using generative modelling
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2303.05927 (cs)
[Submitted on 10 Mar 2023]

Title:Estimating friction coefficient using generative modelling

Authors:Mohammad Otoofi, William J.B. Midgley, Leo Laine, Henderson Leon, Laura Justham, James Fleming
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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.
Comments: To be published in ICM2023
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2303.05927 [cs.CV]
  (or arXiv:2303.05927v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2303.05927

Submission history

From: Mohammad Otoofi [view email]
[v1] Fri, 10 Mar 2023 13:58:28 UTC (4,583 KB)
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