Predicting Aquaplaning Performance from Tyre Profile Images with Machine Learning

  • Tillman Weyde
  • Gregory Slabaugh
  • Gauthier Fontaine
  • Christoph Bederna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7950)


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.


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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fwa, T.F., Kumar, S.S., Anupam, K., Ong, G.P.: Effectiveness of Tire-Tread Patterns in Reducing the Risk of Hydroplaning. Journal of the Transportation Research Board 2094, 91–102 (2009)CrossRefGoogle Scholar
  2. 2.
    Jenq, S.T., Chiu, Y.S., Wu, W.J.: Verification and Analysis of Transient Hydroplaning Performance for Inflated Radial Tire with V-shaped Prototype Grooved Tread Pattern Using LS-DYNA Explicit Interactive FSI Scheme. In: ISEM-ACEM-SEM (2012)Google Scholar
  3. 3.
    Rumelhart, D.E., McClelland, J.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. The MIT Press, Cambridge (1986)Google Scholar
  4. 4.
    Bishop, C.M.: Neural networks for pattern recognition. Clarendon Press, Oxford (1997)Google Scholar
  5. 5.
    Baxter, J.: Learning internal representations. In: Proceedings of the Eighth International Conference on Computational Learning Theory, pp. 311–320. ACM Press (1995)Google Scholar
  6. 6.
    Argyriou, A., Evgeniou, T., Pontil, M., Argyriou, A., Evgeniou, T., Pontil, M.: Convex multi-task feature learning. In: Machine Learning (2007)Google Scholar
  7. 7.
    Chen, J., Liu, J., Ye, J.: Learning incoherent sparse and low-rank patterns from multiple tasks (2010)Google Scholar

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

Personalised recommendations