Supplematary material for "Quo-vadis multi-agent automotive research"

DOI:10.4121/40d7a8c5-2c68-4681-8a15-a224be5ca1fe.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/40d7a8c5-2c68-4681-8a15-a224be5ca1fe

Datacite citation style

Bazilinskyy, Pavlo; Walker, Francesco; Dey, Debargha; Tran, Tram; Park, Hyungchai et. al. (2025): Supplematary material for "Quo-vadis multi-agent automotive research". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/40d7a8c5-2c68-4681-8a15-a224be5ca1fe.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Supplementary material for the paper Bazilinskyy, Pavlo, Walker, Francesco, Dey, Debargha, Tran, Tram T. M., Park, Hyungchai, Kim, Hyochang, Kang, Hyunmin, & Ebel, Patrick (2025), Quo-vadis multi-agent automotive research? Insights from a participatory workshop and questionnaire, Adjunct Proceedings of the 17th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI), Brisbane, QLD, Australia.


Abstract: The transition to mixed-traffic environments that involve automated vehicles, manually operated vehicles, and vulnerable road users presents new challenges for human-centered automotive research. Despite this, most studies in the domain focus on single-agent interactions. This paper reports on a participatory workshop (N = 15) and a questionnaire (N = 19) conducted during the AutomotiveUI '24 conference to explore the state of multi-agent automotive research. The participants discussed methodological challenges and opportunities in real-world settings, simulations, and computational modeling. Key findings reveal that while the value of multi-agent approaches is widely recognized, practical and technical barriers hinder their implementation. The study highlights the need for interdisciplinary methods, better tools, and simulation environments that support scalable, realistic, and ethically informed multi-agent research.

History

  • 2025-08-04 first online, published, posted

Publisher

4TU.ResearchData

Format

csv, pdf, txt, png

Organizations

TU Eindhoven, Department of Industrial Design, Computational Design Systems
Leiden University, Department of Cognitive Psychology
Sydney School of Architecture, Design and Planning, The University of Sydney
Stanford Center at the Incheon Global Campus, Stanford University
ScaDS.AI, Leipzig University

DATA

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