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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

Information

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.

References

Bertoni, A. (2020), “DATA-DRIVEN DESIGN IN CONCEPT DEVELOPMENT: SYSTEMATIC REVIEW AND MISSED OPPORTUNITIES”, Proceedings of the Design Society: DESIGN Conference, Vol. 1, pp. 101–110, https://dx.doi.org/10.1017/dsd.2020.4.Google Scholar
Bozkir, E., Geisler, D. and Kasneci, E. (2019), “Person Independent, Privacy Preserving, and Real Time Assessment of Cognitive Load using Eye Tracking in a Virtual Reality Setup”, 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), presented at the 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 1834–1837, https://dx.doi.org/10.1109/VR.2019.8797758.CrossRefGoogle Scholar
Chromik, M., Lachner, F. and Butz, A. (2020), “ML for UX? - An Inventory and Predictions on the Use of Machine Learning Techniques for UX Research”, Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, presented at the NordiCHI ’20: Shaping Experiences, Shaping Society, ACM, Tallinn Estonia, pp. 1–11, https://dx.doi.org/10.1145/3419249.3420163.CrossRefGoogle Scholar
Clarke, V. and Braun, V. (2017), “Thematic analysis”, The Journal of Positive Psychology, Routledge, Vol. 12 No. 3, pp. 297–298, https://dx.doi.org/10.1080/17439760.2016.1262613.CrossRefGoogle Scholar
Ebel, P., Brokhausen, F. and Vogelsang, A. (2020), “The Role and Potentials of Field User Interaction Data in the Automotive UX Development Lifecycle: An Industry Perspective”, 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, presented at the AutomotiveUI ’20: 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, Virtual Event DC USA, pp. 141–150, https://dx.doi.org/10.1145/3409120.3410638.Google Scholar
Ebel, P., Orlovska, J., Hünemeyer, S., Wickman, C., Vogelsang, A. and Söderberg, R. (2021), “Automotive UX design and data-driven development: Narrowing the gap to support practitioners”, Transportation Research Interdisciplinary Perspectives, Vol. 11, p. 100455, https://dx.doi.org/10.1016/j.trip.2021.100455.CrossRefGoogle Scholar
Ehsan, U., Liao, Q.V., Muller, M., Riedl, M.O. and Weisz, J.D. (2021), “Expanding Explainability: Towards Social Transparency in AI systems”, Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, presented at the CHI ’21: CHI Conference on Human Factors in Computing Systems, ACM, Yokohama Japan, pp. 1–19, https://dx.doi.org/10.1145/3411764.3445188.CrossRefGoogle Scholar
Fan, M., Li, Y. and Truong, K.N. (2020), “Automatic Detection of Usability Problem Encounters in Think-aloud Sessions”, ACM Transactions on Interactive Intelligent Systems, Vol. 10 No. 2, pp. 1–24, https://dx.doi.org/10.1145/3385732.CrossRefGoogle Scholar
Feinberg, M. (2017), “A Design Perspective on Data”, Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, presented at the CHI ’17: CHI Conference on Human Factors in Computing Systems, ACM, Denver Colorado USA, pp. 2952–2963, https://dx.doi.org/10.1145/3025453.3025837.CrossRefGoogle Scholar
François, M., Osiurak, F., Fort, A., Crave, P. and Navarro, J. (2017), “Automotive HMI design and participatory user involvement: review and perspectives”, Ergonomics, Taylor & Francis, Vol. 60 No. 4, pp. 541–552, https://dx.doi.org/10.1080/00140139.2016.1188218.Google ScholarPubMed
Gajjar, N., Sermuga Pandian, V.P., Suleri, S. and Jarke, M. (2021), “Akin: Generating UI Wireframes From UI Design Patterns Using Deep Learning”, 26th International Conference on Intelligent User Interfaces - Companion, Association for Computing Machinery, New York, NY, USA, pp. 40–42, https://dx.doi.org/10.1145/3397482.3450727.CrossRefGoogle Scholar
Gorkovenko, K., Burnett, D.J., Thorp, J.K., Richards, D. and Murray-Rust, D. (2020), “Exploring The Future of Data-Driven Product Design”, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, presented at the CHI ’20: CHI Conference on Human Factors in Computing Systems, ACM, Honolulu HI USA, pp. 1–14, https://dx.doi.org/10.1145/3313831.3376560.CrossRefGoogle Scholar
Guest, G., Namey, E. and Chen, M. (2020), “A simple method to assess and report thematic saturation in qualitative research”, PLOS ONE, Public Library of Science, Vol. 15 No. 5, p. e0232076, https://dx.doi.org/10.1371/journal.pone.0232076.CrossRefGoogle ScholarPubMed
Güldenpfennig, F., Mayer, P., Panek, P. and Fitzpatrick, G. (2019), “An Autonomy-Perspective on the Design of Assistive Technology Experiences of People with Multiple Sclerosis”, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, pp. 1–14, https://dx.doi.org/10.1145/3290605.3300357.CrossRefGoogle Scholar
Herrmann, T. and Pfeiffer, S. (2023), “Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence”, AI & SOCIETY, Vol. 38 No. 4, pp. 1523–1542, https://dx.doi.org/10.1007/s00146-022-01391-5.CrossRefGoogle Scholar
Huang, F., Li, G., Zhou, X., Canny, J.F. and Li, Y. (2021), “Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models”, arXiv, 14 October, https://dx.doi.org/10.48550/arXiv.2110.07775.Google Scholar
Jiang, Y., Lu, Y., Nichols, J., Stuerzlinger, W., Yu, C., Lutteroth, C., Li, Y., et al. (2022), “Computational Approaches for Understanding, Generating, and Adapting User Interfaces”, CHI Conference on Human Factors in Computing Systems Extended Abstracts, presented at the CHI ’22: CHI Conference on Human Factors in Computing Systems, ACM, New Orleans LA USA, pp. 1–6, https://dx.doi.org/10.1145/3491101.3504030.CrossRefGoogle Scholar
Joshi, A., Attia, Y. and Mishra, T. (2019), “Protocol for Eliciting Driver Frustration in an In-vehicle Environment”, 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), presented at the 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 620–626, https://dx.doi.org/10.1109/ACII.2019.8925489.CrossRefGoogle Scholar
Lu, Y., Zhang, C., Zhang, I. and Li, T.J.-J. (2022), “Bridging the Gap Between UX Practitioners’ Work Practices and AI-Enabled Design Support Tools”, Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, pp. 1–7, https://dx.doi.org/10.1145/3491101.3519809.CrossRefGoogle Scholar
Mohamed, S., Png, M.-T. and Isaac, W. (2020), “Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence”, Philosophy & Technology, Vol. 33 No. 4, pp. 659–684, https://dx.doi.org/10.1007/s13347-020-00405-8.CrossRefGoogle Scholar
Murphy, K.P. (2012), Machine Learning: A Probabilistic Perspective, MIT Press.Google Scholar
Parker, C., Scott, S. and Geddes, A. (2019), “Snowball Sampling”, https://dx.doi.org/10.4135/9781526421036831710.CrossRefGoogle Scholar
Salminen, J., Guan, K., Jung, S.-G., Chowdhury, S.A. and Jansen, B.J. (2020), “A Literature Review of Quantitative Persona Creation”, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, presented at the CHI ’20: CHI Conference on Human Factors in Computing Systems, ACM, Honolulu HI USA, pp. 1–14, https://dx.doi.org/10.1145/3313831.3376502.CrossRefGoogle Scholar
Shneiderman, B. (2020), “Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy”, International Journal of Human–Computer Interaction, Taylor & Francis, Vol. 36 No. 6, pp. 495–504, https://dx.doi.org/10.1080/10447318.2020.1741118.CrossRefGoogle Scholar
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M. and Elger, G. (2021), “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry”, Reliability Engineering & System Safety, Vol. 215, p. 107864, https://dx.doi.org/10.1016/j.ress.2021.107864.CrossRefGoogle Scholar
Usama, M., Arif, A., Haris, F., Khan, S., Afaq, S.K. and Rashid, S. (2021), “A Data-Driven Interactive System for Aerodynamic and User-centred Generative Vehicle Design”, 2021 International Conference on Artificial Intelligence (ICAI), presented at the 2021 International Conference on Artificial Intelligence (ICAI), pp. 119–127, https://dx.doi.org/10.1109/ICAI52203.2021.9445243.CrossRefGoogle Scholar
Vaughan, J.W. and Wallach, H. (2021), “A Human-Centered Agenda for Intelligible Machine Learning”, in Pelillo, M. and Scantamburlo, T. (Eds.), Machines We Trust, The MIT Press, pp. 123–138, https://dx.doi.org/10.7551/mitpress/12186.003.0014.CrossRefGoogle Scholar
Wang, L. and Liu, Z. (2021), “Data-driven product design evaluation method based on multi-stage artificial neural network”, Applied Soft Computing, Vol. 103, p. 107117, https://dx.doi.org/10.1016/j.asoc.2021.107117.CrossRefGoogle Scholar
Wang, Z., Zheng, P., Li, X. and Chen, C.-H. (2022), “Implications of data-driven product design: From information age towards intelligence age”, Advanced Engineering Informatics, Vol. 54, p. 101793, https://dx.doi.org/10.1016/j.aei.2022.101793.CrossRefGoogle Scholar
Yadav, O.P. and Goel, P.S. (2008), “Customer satisfaction driven quality improvement target planning for product development in automotive industry”, International Journal of Production Economics, Vol. 113 No. 2, pp. 997–1011, https://dx.doi.org/10.1016/j.ijpe.2007.12.008.CrossRefGoogle Scholar
Yang, Y., Wu, X. and Zhu, X. (2005), “Combining proactive and reactive predictions for data streams”, Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, presented at the KDD05: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Chicago Illinois USA, pp. 710–715, https://dx.doi.org/10.1145/1081870.1081961.CrossRefGoogle Scholar
Yurtsever, E., Lambert, J., Carballo, A. and Takeda, K. (2020), “A Survey of Autonomous Driving: Common Practices and Emerging Technologies”, IEEE Access, presented at the IEEE Access, Vol. 8, pp. 58443–58469, https://dx.doi.org/10.1109/ACCESS.2020.2983149.Google Scholar