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Work domain modeling of human-automation interaction for in-vehicle automation.

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Title: Work domain modeling of human-automation interaction for in-vehicle automation.
Authors: Zhang, You1 (AUTHOR), Lintern, Gavan2 (AUTHOR) Gavan.Lintern@monash.edu
Source: Cognition, Technology & Work. Nov2024, Vol. 26 Issue 4, p585-601. 17p.
Subjects: Automobile driving simulators, Cognitive analysis, Automation, Scientific observation, Responsibility
Abstract: Automated driving systems are deployed on public roads with little empirical support for the dominant justifications of enhanced safety and enhanced productivity. Furthermore, development of automated driving systems has been piecemeal rather than systematic while research on driver-automation interaction has relied on individual analysis of accidents and on observational studies of driving behavior in a simulator or on the road. In this paper, we apply Work Domain Analysis to develop a more systematic and comprehensive model of automated driving. We use a strategy of layering the driving automation onto the resulting Abstraction-Decomposition Space for manual driving to mimic the existing design strategy of introducing automation to take over driving functions previously the responsibility of the human driver. Our analysis shows that automation does not unequivocally supports dominant driving values. Furthermore, our analysis revealed subtle interdependencies between human and technological functions. We conclude that an Abstraction Decomposition Space offers a systematic view of driver-automation interaction that can suggest new insights for automation design. [ABSTRACT FROM AUTHOR]
Copyright of Cognition, Technology & Work is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Work domain modeling of human-automation interaction for in-vehicle automation.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+You%22">Zhang, You</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lintern%2C+Gavan%22">Lintern, Gavan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> Gavan.Lintern@monash.edu</i>
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  Data: <searchLink fieldCode="DE" term="%22Automobile+driving+simulators%22">Automobile driving simulators</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+analysis%22">Cognitive analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+observation%22">Scientific observation</searchLink><br /><searchLink fieldCode="DE" term="%22Responsibility%22">Responsibility</searchLink>
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  Data: Automated driving systems are deployed on public roads with little empirical support for the dominant justifications of enhanced safety and enhanced productivity. Furthermore, development of automated driving systems has been piecemeal rather than systematic while research on driver-automation interaction has relied on individual analysis of accidents and on observational studies of driving behavior in a simulator or on the road. In this paper, we apply Work Domain Analysis to develop a more systematic and comprehensive model of automated driving. We use a strategy of layering the driving automation onto the resulting Abstraction-Decomposition Space for manual driving to mimic the existing design strategy of introducing automation to take over driving functions previously the responsibility of the human driver. Our analysis shows that automation does not unequivocally supports dominant driving values. Furthermore, our analysis revealed subtle interdependencies between human and technological functions. We conclude that an Abstraction Decomposition Space offers a systematic view of driver-automation interaction that can suggest new insights for automation design. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Cognition, Technology & Work is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10111-024-00780-8
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 585
    Subjects:
      – SubjectFull: Automobile driving simulators
        Type: general
      – SubjectFull: Cognitive analysis
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Scientific observation
        Type: general
      – SubjectFull: Responsibility
        Type: general
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      – TitleFull: Work domain modeling of human-automation interaction for in-vehicle automation.
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            NameFull: Zhang, You
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            NameFull: Lintern, Gavan
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              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
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              Value: 26
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            – TitleFull: Cognition, Technology & Work
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