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Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel

OBJECTIVE: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation. BACKGROUND: Securing driver engagement—by mitigating irony of automation (i.e., the better the automation, th...

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Autores principales: Victor, Trent W., Tivesten, Emma, Gustavsson, Pär, Johansson, Joel, Sangberg, Fredrik, Ljung Aust, Mikael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6207994/
https://www.ncbi.nlm.nih.gov/pubmed/30096002
http://dx.doi.org/10.1177/0018720818788164
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author Victor, Trent W.
Tivesten, Emma
Gustavsson, Pär
Johansson, Joel
Sangberg, Fredrik
Ljung Aust, Mikael
author_facet Victor, Trent W.
Tivesten, Emma
Gustavsson, Pär
Johansson, Joel
Sangberg, Fredrik
Ljung Aust, Mikael
author_sort Victor, Trent W.
collection PubMed
description OBJECTIVE: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation. BACKGROUND: Securing driver engagement—by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions—is a major challenge in the human factors literature. METHOD: One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag. RESULTS: Supervision reminders effectively maintained drivers’ eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag). CONCLUSION: The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel. APPLICATION: Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control.
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spelling pubmed-62079942018-11-21 Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel Victor, Trent W. Tivesten, Emma Gustavsson, Pär Johansson, Joel Sangberg, Fredrik Ljung Aust, Mikael Hum Factors Automation, Expert Systems OBJECTIVE: The aim of this study was to understand how to secure driver supervision engagement and conflict intervention performance while using highly reliable (but not perfect) automation. BACKGROUND: Securing driver engagement—by mitigating irony of automation (i.e., the better the automation, the less attention drivers will pay to traffic and the system, and the less capable they will be to resume control) and by communicating system limitations to avoid mental model misconceptions—is a major challenge in the human factors literature. METHOD: One hundred six drivers participated in three test-track experiments in which we studied driver intervention response to conflicts after driving highly reliable but supervised automation. After 30 min, a conflict occurred wherein the lead vehicle cut out of lane to reveal a conflict object in the form of either a stationary car or a garbage bag. RESULTS: Supervision reminders effectively maintained drivers’ eyes on path and hands on wheel. However, neither these reminders nor explicit instructions on system limitations and supervision responsibilities prevented 28% (21/76) of drivers from crashing with their eyes on the conflict object (car or bag). CONCLUSION: The results uncover the important role of expectation mismatches, showing that a key component of driver engagement is cognitive (understanding the need for action), rather than purely visual (looking at the threat), or having hands on wheel. APPLICATION: Automation needs to be designed either so that it does not rely on the driver or so that the driver unmistakably understands that it is an assistance system that needs an active driver to lead and share control. SAGE Publications 2018-08-10 2018-12 /pmc/articles/PMC6207994/ /pubmed/30096002 http://dx.doi.org/10.1177/0018720818788164 Text en © 2018, Human Factors and Ergonomics Society http://www.creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Automation, Expert Systems
Victor, Trent W.
Tivesten, Emma
Gustavsson, Pär
Johansson, Joel
Sangberg, Fredrik
Ljung Aust, Mikael
Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel
title Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel
title_full Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel
title_fullStr Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel
title_full_unstemmed Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel
title_short Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel
title_sort automation expectation mismatch: incorrect prediction despite eyes on threat and hands on wheel
topic Automation, Expert Systems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6207994/
https://www.ncbi.nlm.nih.gov/pubmed/30096002
http://dx.doi.org/10.1177/0018720818788164
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