Literature DB >> 17063963

Automation reliability in unmanned aerial vehicle control: a reliance-compliance model of automation dependence in high workload.

Stephen R Dixon1, Christopher D Wickens.   

Abstract

OBJECTIVE: Two experiments were conducted in which participants navigated a simulated unmanned aerial vehicle (UAV) through a series of mission legs while searching for targets and monitoring system parameters. The goal of the study was to highlight the qualitatively different effects of automation false alarms and misses as they relate to operator compliance and reliance, respectively.
BACKGROUND: Background data suggest that automation false alarms cause reduced compliance, whereas misses cause reduced reliance.
METHOD: In two studies, 32 and 24 participants, including some licensed pilots, performed in-lab UAV simulations that presented the visual world and collected dependent measures.
RESULTS: Results indicated that with the low-reliability aids, false alarms correlated with poorer performance in the system failure task, whereas misses correlated with poorer performance in the concurrent tasks.
CONCLUSION: Compliance and reliance do appear to be affected by false alarms and misses, respectively, and are relatively independent of each other. APPLICATION: Practical implications are that automated aids must be fairly reliable to provide global benefits and that false alarms and misses have qualitatively different effects on performance.

Entities:  

Mesh:

Year:  2006        PMID: 17063963     DOI: 10.1518/001872006778606822

Source DB:  PubMed          Journal:  Hum Factors        ISSN: 0018-7208            Impact factor:   2.888


  6 in total

Review 1.  Automation bias: a systematic review of frequency, effect mediators, and mitigators.

Authors:  Kate Goddard; Abdul Roudsari; Jeremy C Wyatt
Journal:  J Am Med Inform Assoc       Date:  2011-06-16       Impact factor: 4.497

2.  Prospective memory in an air traffic control simulation: external aids that signal when to act.

Authors:  Shayne Loft; Rebekah E Smith; Adella Bhaskara
Journal:  J Exp Psychol Appl       Date:  2011-03

3.  Understanding human management of automation errors.

Authors:  Sara E McBride; Wendy A Rogers; Arthur D Fisk
Journal:  Theor Issues Ergon Sci       Date:  2014

4.  Understanding the effect of workload on automation use for younger and older adults.

Authors:  Sara E McBride; Wendy A Rogers; Arthur D Fisk
Journal:  Hum Factors       Date:  2011-12       Impact factor: 2.888

5.  Supporting dynamic change detection: using the right tool for the task.

Authors:  Benoît R Vallières; Helen M Hodgetts; François Vachon; Sébastien Tremblay
Journal:  Cogn Res Princ Implic       Date:  2016-12-19

Review 6.  Automation bias and verification complexity: a systematic review.

Authors:  David Lyell; Enrico Coiera
Journal:  J Am Med Inform Assoc       Date:  2017-03-01       Impact factor: 4.497

  6 in total

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