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Design of Proactive Interaction for In-Vehicle Robots Based on Transparency

Based on the transparency theory, this study investigates the appropriate amount of transparency information expressed by the in-vehicle robot under two channels of voice and visual in a proactive interaction scenario. The experiments are to test and evaluate different transparency levels and combin...

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Detalles Bibliográficos
Autores principales: Wang, Jianmin, Yue, Tianyang, Liu, Yujia, Wang, Yuxi, Wang, Chengji, Yan, Fei, You, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146175/
https://www.ncbi.nlm.nih.gov/pubmed/35632284
http://dx.doi.org/10.3390/s22103875
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author Wang, Jianmin
Yue, Tianyang
Liu, Yujia
Wang, Yuxi
Wang, Chengji
Yan, Fei
You, Fang
author_facet Wang, Jianmin
Yue, Tianyang
Liu, Yujia
Wang, Yuxi
Wang, Chengji
Yan, Fei
You, Fang
author_sort Wang, Jianmin
collection PubMed
description Based on the transparency theory, this study investigates the appropriate amount of transparency information expressed by the in-vehicle robot under two channels of voice and visual in a proactive interaction scenario. The experiments are to test and evaluate different transparency levels and combinations of information in different channels of the in-vehicle robot, based on a driving simulator to collect subjective and objective data, which focuses on users’ safety, usability, trust, and emotion dimensions under driving conditions. The results show that appropriate transparency expression is able to improve drivers’ driving control and subjective evaluation and that drivers need a different amount of transparency information in different types of tasks.
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spelling pubmed-91461752022-05-29 Design of Proactive Interaction for In-Vehicle Robots Based on Transparency Wang, Jianmin Yue, Tianyang Liu, Yujia Wang, Yuxi Wang, Chengji Yan, Fei You, Fang Sensors (Basel) Article Based on the transparency theory, this study investigates the appropriate amount of transparency information expressed by the in-vehicle robot under two channels of voice and visual in a proactive interaction scenario. The experiments are to test and evaluate different transparency levels and combinations of information in different channels of the in-vehicle robot, based on a driving simulator to collect subjective and objective data, which focuses on users’ safety, usability, trust, and emotion dimensions under driving conditions. The results show that appropriate transparency expression is able to improve drivers’ driving control and subjective evaluation and that drivers need a different amount of transparency information in different types of tasks. MDPI 2022-05-20 /pmc/articles/PMC9146175/ /pubmed/35632284 http://dx.doi.org/10.3390/s22103875 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Jianmin
Yue, Tianyang
Liu, Yujia
Wang, Yuxi
Wang, Chengji
Yan, Fei
You, Fang
Design of Proactive Interaction for In-Vehicle Robots Based on Transparency
title Design of Proactive Interaction for In-Vehicle Robots Based on Transparency
title_full Design of Proactive Interaction for In-Vehicle Robots Based on Transparency
title_fullStr Design of Proactive Interaction for In-Vehicle Robots Based on Transparency
title_full_unstemmed Design of Proactive Interaction for In-Vehicle Robots Based on Transparency
title_short Design of Proactive Interaction for In-Vehicle Robots Based on Transparency
title_sort design of proactive interaction for in-vehicle robots based on transparency
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146175/
https://www.ncbi.nlm.nih.gov/pubmed/35632284
http://dx.doi.org/10.3390/s22103875
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