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Levels of What? Investigating Drivers' Understanding of Different Levels of Automation in Vehicles
Volvo Car Corporation, Göteborg, Sweden; Chalmers University of Technology, Göteborg, Sweden.ORCID iD: 0000-0001-6381-2346
Chalmers University of Technology, Göteborg, Sweden.
Chalmers University of Technology, Göteborg, Sweden.
Chalmers University of Technology, Göteborg, Sweden.
2021 (English)In: Journal of Cognitive Engineering and Decision Making, ISSN 1555-3434, E-ISSN 2169-5032, Vol. 15, no 2-3, p. 116-132Article in journal (Refereed) Published
Abstract [en]

Extant levels of automation (LoAs) taxonomies describe variations in function allocations between the driver and the driving automation system (DAS) from a technical perspective. However, these taxonomies miss important human factors issues and when design decisions are based on them, the resulting interaction design leaves users confused. Therefore, the aim of this paper is to describe how users perceive different DASs by eliciting insights from an empirical driving study facilitating a Wizard-of-Oz approach, where 20 participants were interviewed after experiencing systems on two different LoAs under real driving conditions. The findings show that participants talked about the DAS by describing different relationships and dependencies between three different elements: the context (traffic conditions, road types), the vehicle (abilities, limitations, vehicle operations), and the driver (control, attentional demand, interaction with displays and controls, operation of vehicle), each with associated aspects that indicate what users identify as relevant when describing a vehicle with automated systems. Based on these findings, a conceptual model is proposed by which designers can differentiate LoAs from a human-centric perspective and that can aid in the development of design guidelines for driving automation.

Place, publisher, year, edition, pages
Sage Publications, 2021. Vol. 15, no 2-3, p. 116-132
Keywords [en]
levels of automation, vehicle automation, automated driving, human-centric, empirical study, user study
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:oru:diva-117132DOI: 10.1177/15553434211009024ISI: 000643474300001Scopus ID: 2-s2.0-85104435218OAI: oai:DiVA.org:oru-117132DiVA, id: diva2:1910320
Funder
Vinnova, 2017-01946Chalmers University of TechnologyAvailable from: 2024-11-04 Created: 2024-11-04 Last updated: 2024-11-05Bibliographically approved

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Novakazi, Fjollë

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