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Assessment selection in human-automation interaction studies: The Failure-GAM2E and review of assessment methods for highly automated driving
Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Human Work Science.ORCID iD: 0000-0003-1705-8615
2017 (English)In: Applied Ergonomics, ISSN 0003-6870, E-ISSN 1872-9126Article in journal (Refereed) Epub ahead of print
Abstract [en]

Highly automated driving will change driver's behavioural patterns. Traditional methods used for assessing manual driving will only be applicable for the parts of human-automation interaction where the driver intervenes such as in hand-over and take-over situations. Therefore, driver behaviour assessment will need to adapt to the new driving scenarios. This paper aims at simplifying the process of selecting appropriate assessment methods. Thirty-five papers were reviewed to examine potential and relevant methods. The review showed that many studies still relies on traditional driving assessment methods. A new method, the Failure-GAM2E model, with purpose to aid assessment selection when planning a study, is proposed and exemplified in the paper. Failure-GAM2E includes a systematic step-by-step procedure defining the situation, failures (Failure), goals (G), actions (A), subjective methods (M), objective methods (M) and equipment (E). The use of Failure-GAM2E in a study example resulted in a well-reasoned assessment plan, a new way of measuring trust through feet movements and a proposed Optimal Risk Management Model. Failure-GAM2E and the Optimal Risk Management Model are believed to support the planning process for research studies in the field of human-automation interaction.

Place, publisher, year, edition, pages
Elsevier, 2017.
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Engineering Psychology
Identifiers
URN: urn:nbn:se:ltu:diva-65469DOI: 10.1016/j.apergo.2017.08.010PubMedID: 28865841OAI: oai:DiVA.org:ltu-65469DiVA: diva2:1138141
Available from: 2017-09-04 Created: 2017-09-04 Last updated: 2017-09-11

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