# Estimating friction coefficient using generative modelling

Source: [Estimating friction coefficient using generative modelling](<https://www.sota2.com/research/paper/68fa6ec21e209131cea41d02>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:21:27.234Z

Authors: Mohammad Otoofi, William J.B. Midgley, Leo Laine, Henderson Leon, Laura Justham, James Fleming

Published: 2023-03-10T13:58:28.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2303.05927>)

[PDF](<https://arxiv.org/pdf/2303.05927.pdf>)