Estimating friction coefficient using generative modelling
This work addresses friction estimation for autonomous vehicles or robotics, but it appears incremental as it builds on existing semantic segmentation methods.
The authors tackled the problem of estimating tyre-road friction by reformulating it as a visual perceptual learning task, using semantic segmentation to detect surface characteristics and predict frictional force, with preliminary results showing the approach can estimate frictional force.
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.