Estimating friction coefficient using generative modelling
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Abstract (arXiv)
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.
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References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2019A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguitiescited by 65
- 2020Robust Semantic Segmentation in Adverse Weather Conditions by means of Fast Video-Sequence Segmentationcited by 4
- 2019Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Datacited by 1
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