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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9146175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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