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

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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.

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Acknowledgements

This project is co-sponsored by HORIBA MIRA Ltd. and Coventry University and is a part of a PhD programme at Coventry University. Authors would like to thank HORIBA MIRA staff for participation in this research. We have no conflict of interest.

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Authors and Affiliations

  1. Research Institute Future Transport and Cities, Coventry University, Priory Street, Coventry, CV1 5FB, UK

    M. Cieslak, S. Kanarachos & M. Blundell

  2. HORIBA MIRA Ltd., Watling Street, Nuneaton, CV10 0TU, UK

    M. Burnett & A. Baxendale

  3. Intelligent Mobility Design Centre, Royal College of Art, Kensington Gore, London, SW7 2EU, UK

    C. Diels

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  1. M. Cieslak
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  2. S. Kanarachos
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Cieslak, M., Kanarachos, S., Blundell, M. et al. Accurate ride comfort estimation combining accelerometer measurements, anthropometric data and neural networks. Neural Comput & Applic 32, 8747–8762 (2020). https://doi.org/10.1007/s00521-019-04351-1

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