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Navigating from data-driven design to designing with ML: a case study of truck HMI system design

Published online by Cambridge University Press:  16 May 2024

Yi Luo ,
Dimitrios Gkouskos ,
Nancy L. Russo  and
Minjuan Wang
Yi Luo*
Affiliation:
Halmstad University, Sweden
Dimitrios Gkouskos
Affiliation:
Halmstad University, Sweden
Nancy L. Russo
Affiliation:
Halmstad University, Sweden Malmö University, Sweden
Minjuan Wang
Affiliation:
Halmstad University, Sweden
*
yi.luo@hh.se

Abstract

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Data-driven design is believed to be empowered by machine learning (ML) with advanced pattern classification and prediction. However, research on how ML can be used to support automotive human-machine interface (HMI) design is lacking. We presented a case study of truck HMI design to understand the current data use and expectations of ML in the design process. Findings show decentralized data practices, the role of expertise in decision-making, and the envisioned reactive use of ML, where we underscore the implications for advancing human-ML collaboration in designing future truck HMI systems.

Keywords

data-driven designdesign processmachine learningtruck HMIML user needs
Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2119 - 2128
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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