Predicting Aquaplaning Performance from Tyre Profile Images with Machine Learning

  • Tillman Weyde
  • Gregory Slabaugh
  • Gauthier Fontaine
  • Christoph Bederna
Conference paper

DOI: 10.1007/978-3-642-39094-4_16

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7950)
Cite this paper as:
Weyde T., Slabaugh G., Fontaine G., Bederna C. (2013) Predicting Aquaplaning Performance from Tyre Profile Images with Machine Learning. In: Kamel M., Campilho A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg

Abstract

The tread of a tyre consists of a profile (pattern of grooves, sipes, and blocks) mainly designed to improve wet performance and inhibit aquaplaning by providing a conduit for water to be expelled underneath the tyre as it makes contact with the road surface. Testing different tread profile designs is time consuming, as it requires fabrication and physical measurement of tyres. We propose a supervised machine learning method to predict tyres’ aquaplaning performance based on the tread profile described in geometry and rubber stiffness. Our method provides a regressor from the space of profile geometry, reduced to images, to aquaplaning performance. Experimental results demonstrate that image analysis and machine learning combined with other methods can yield improved prediction of aquaplaning performance, even using non-normalised data. Therefore this method has can potentially save substantial cost and time in tyre development. This investigation is based on data provided by Continental Reifen Deutschland GmbH.

Keywords

Image analysis supervised machine learning non-normalised data tyre profile regression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tillman Weyde
    • 1
  • Gregory Slabaugh
    • 1
  • Gauthier Fontaine
    • 2
  • Christoph Bederna
    • 3
  1. 1.City University LondonUK
  2. 2.ENSTA ParistechFrance
  3. 3.Continental AGGermany

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