Accurate ride comfort estimation combining accelerometer measurements, anthropometric data and neural networks

Maciej Piotr Cieslak, Stratis Kanarachos, Cyriel Diels, Mike Blundell, Anthony Baxendale, Mark Burnett

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Abstract

Ride comfort can heavily influence user experience and therefore comprises one of the most important vehicle design targets. Although ride comfort has been heavily researched, there is still no definite solution to its accurate estimation. This can be attributed, to a large extent, to the subjective nature of the problem. Aim of this study was to explore the use of neural networks for the accurate estimation of ride comfort by combining anthropometric data and acceleration measurements. Different acceleration inputs, neural network architectures, training algorithms and objective functions were systematically investigated, and optimal parameters were derived. New insight into the influence of anthropometric data on ride comfort has been gained. The results indicate that the proposed method improves the accuracy of subjective ride comfort estimation compared to current standards. Neural networks were trained using data derived from a range of field trials involving ten participants, on public roads and controlled environment. A clustering and sensitivity analysis complements the study and identifies the most important factors influencing subjective ride comfort evaluation.

Original languageEnglish
Pages (from-to)8747–8762
Number of pages16
JournalNeural Computing and Applications
Volume32
Issue number12
Early online date30 Jul 2019
DOIs
Publication statusPublished - Jun 2020

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-019-04351-1
Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

  • Comfort perception
  • Neural networks
  • Ride
  • Vibration

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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