Estimating friction coefficient using generative modelling
Estimating friction coefficient using generative modelling
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DOI：
10.1109/icm54990.2023.10101932
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Mohammad Otoofi;William J. B. Midgley;L. Laine;Henderson Leon;L. Justham;James Fleming
中科院分区：
文献类型：
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作者：
Mohammad Otoofi;William J. B. Midgley;L. Laine;Henderson Leon;L. Justham;James Fleming
通常利用动态模型来实时测量轮胎与路面的摩擦力。或者，预测方法通过识别影响轮胎-路面摩擦力的环境因素来估计轮胎-路面摩擦力。这项工作旨在将摩擦估计问题表述为视觉感知学习任务。该问题被分解为通过应用语义分割来检测表面特征并使用提取的特征来预测摩擦力。这项工作首次将摩擦估计问题表述为语义分割模型潜在空间的回归。初步结果表明该方法可以估计摩擦力。
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.