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Shaping Autonomous Vehicles: Towards a Taxonomy of Design Features Instilling a Sense of Safety
ntelligent Mobility Design Centre, Royal College of Art, London, (GBR); Laboratory for Artificial Intelligence in Design, Hong Kong, (HKG).
University West, School of Business, Economics and IT, Division of Media and Design. Department of Industrial and Materials Science, Chalmers University of Technology, 412 96, Gothenburg (SWE). (LINA iAIL)
Intelligent Mobility Design Centre, Royal College of Art, London (GBR).
Intelligent Mobility Design Centre, Royal College of Art, London, (GBR); Laboratory for Artificial Intelligence in Design, Hong Kong, (HKG).
2022 (English)In: Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937, Vol. 1583 CCIS, p. 172-180Article in journal (Refereed) Published
Abstract [en]

Autonomous vehicles (AVs) are Artificial Intelligence (AI)-enabled service robots. Whilst having the potential to enhance our transport systems and journey experiences, there are concerns that the public may be reluctant to adopt AVs, largely driven by doubts about their safety. In this study, we focussed on the role of the exterior vehicle design to instil a sense of safety on behalf of the passenger and bystander, i.e. pedestrians and cyclists. Senior automotive and transport designers were interviewed to identify key design features which revealed a common understanding of key features but also an apparent dichotomy or incompatibility in terms of design directions when considering passengers versus bystanders. Furthermore, designers’ understanding was largely based on their experience of conventional vehicles leading to uncertainty as to the validity in the context of future AVs. The results provide an initial set of design features that will be tested and evaluated with prospective AV users to explore the potential knowledge gap between designers’ intentions and customers’ expectation. This will provide design practitioners tangible, relatable anchors to direct activities towards critical design features whilst enabling design management to introduce more objectivity in their decision making.  

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2022. Vol. 1583 CCIS, p. 172-180
Keywords [en]
Decision making; Intelligent robots; Pedestrian safety; Automotives; Autonomous Vehicles; Design features; Exterior designs; Key feature; Safety perception; Service robots; Transport systems; Uncertainty; Vehicle design; Autonomous vehicles
National Category
Embedded Systems
Research subject
Work Integrated Learning
Identifiers
URN: urn:nbn:se:hv:diva-19195DOI: 10.1007/978-3-031-06394-7_24Scopus ID: 2-s2.0-85133202796OAI: oai:DiVA.org:hv-19195DiVA, id: diva2:1716172
Conference
24th International Conference on Human-Computer Interaction, HCI International, HCII 2022; Conference date: 26 June 2022 through 1 July 2022; Conference code: 279199
Available from: 2022-12-05 Created: 2022-12-05 Last updated: 2023-01-25Bibliographically approved

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Stylidis, Kostas

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