A Case Study of Trust on Autonomous Driving

Shili Sheng; Erfan Pakdamanian; Kyungtae Han; Baek Gyu Kim; Prashant Tiwari; Inki Kim; Lu Feng

doi:10.1109/ITSC.2019.8917251

A Case Study of Trust on Autonomous Driving

Shili Sheng, Erfan Pakdamanian, Kyungtae Han, Baek Gyu Kim, Prashant Tiwari, Inki Kim, Lu Feng

Department of Electrical Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Scopus citations

Abstract

As autonomous vehicles have benefited the society, understanding the dynamic change of humans' trust during human-autonomous vehicle interaction can help to improve the safety and performance of autonomous driving. We designed and conducted a human subjects study involving 19 participants. Each participant was asked to enter their trust level in a Likert scale in real-time during experiments on a driving simulator. We also collected physiological data (e.g., heart rate, pupil size) of participants as complementary indicators of trust. We used analysis of variance (ANOVA) and Signal Temporal Logic (STL) to analyze the experimental data. Our results show the influence of different factors (e.g., automation alarms, weather conditions) on trust, and the individual variability in human reaction time and trust change.

Original language	English
Title of host publication	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4368-4373
Number of pages	6
ISBN (Electronic)	9781538670248
DOIs	https://doi.org/10.1109/ITSC.2019.8917251
State	Published - Oct 2019
Event	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand Duration: 27 Oct 2019 → 30 Oct 2019

Publication series

Name	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Country/Territory	New Zealand
City	Auckland
Period	27/10/19 → 30/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Access to Document

10.1109/ITSC.2019.8917251

Cite this

Sheng, S., Pakdamanian, E., Han, K., Kim, B. G., Tiwari, P., Kim, I., & Feng, L. (2019). A Case Study of Trust on Autonomous Driving. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 (pp. 4368-4373). Article 8917251 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2019.8917251

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title = "A Case Study of Trust on Autonomous Driving",

abstract = "As autonomous vehicles have benefited the society, understanding the dynamic change of humans' trust during human-autonomous vehicle interaction can help to improve the safety and performance of autonomous driving. We designed and conducted a human subjects study involving 19 participants. Each participant was asked to enter their trust level in a Likert scale in real-time during experiments on a driving simulator. We also collected physiological data (e.g., heart rate, pupil size) of participants as complementary indicators of trust. We used analysis of variance (ANOVA) and Signal Temporal Logic (STL) to analyze the experimental data. Our results show the influence of different factors (e.g., automation alarms, weather conditions) on trust, and the individual variability in human reaction time and trust change.",

author = "Shili Sheng and Erfan Pakdamanian and Kyungtae Han and Kim, {Baek Gyu} and Prashant Tiwari and Inki Kim and Lu Feng",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 ; Conference date: 27-10-2019 Through 30-10-2019",

year = "2019",

month = oct,

doi = "10.1109/ITSC.2019.8917251",

language = "English",

series = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",

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Sheng, S, Pakdamanian, E, Han, K, Kim, BG, Tiwari, P, Kim, I & Feng, L 2019, A Case Study of Trust on Autonomous Driving. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019., 8917251, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 4368-4373, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Auckland, New Zealand, 27/10/19. https://doi.org/10.1109/ITSC.2019.8917251

A Case Study of Trust on Autonomous Driving. / Sheng, Shili; Pakdamanian, Erfan; Han, Kyungtae et al.
2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 4368-4373 8917251 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - A Case Study of Trust on Autonomous Driving

AU - Sheng, Shili

AU - Pakdamanian, Erfan

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AU - Feng, Lu

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N2 - As autonomous vehicles have benefited the society, understanding the dynamic change of humans' trust during human-autonomous vehicle interaction can help to improve the safety and performance of autonomous driving. We designed and conducted a human subjects study involving 19 participants. Each participant was asked to enter their trust level in a Likert scale in real-time during experiments on a driving simulator. We also collected physiological data (e.g., heart rate, pupil size) of participants as complementary indicators of trust. We used analysis of variance (ANOVA) and Signal Temporal Logic (STL) to analyze the experimental data. Our results show the influence of different factors (e.g., automation alarms, weather conditions) on trust, and the individual variability in human reaction time and trust change.

AB - As autonomous vehicles have benefited the society, understanding the dynamic change of humans' trust during human-autonomous vehicle interaction can help to improve the safety and performance of autonomous driving. We designed and conducted a human subjects study involving 19 participants. Each participant was asked to enter their trust level in a Likert scale in real-time during experiments on a driving simulator. We also collected physiological data (e.g., heart rate, pupil size) of participants as complementary indicators of trust. We used analysis of variance (ANOVA) and Signal Temporal Logic (STL) to analyze the experimental data. Our results show the influence of different factors (e.g., automation alarms, weather conditions) on trust, and the individual variability in human reaction time and trust change.

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A Case Study of Trust on Autonomous Driving

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