abstract

Trust In Unmanned Driving System

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HRI'15 Extended Abstracts: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts

Pages 7 - 8

https://doi.org/10.1145/2701973.2702023

Published: 02 March 2015 Publication History

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Abstract

This study proposes a between-subject experiment with four conditions representing different levels of anthropomorphism and automation embedded in unmanned driving systems. Participants will be exposed to either a humanoid robot (high anthropomorphism) or a smartphone (low anthropomorphism) that have high and low automation level respectively as an independent driving agent. The study argues that the agent with high level of anthropomorphism and low level of automation is more likely to trigger greater feelings of trust and perceived safety, which then leads to positive perceptions of the system.

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Cited By

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Ma CLiu JHe SHong WShi J(2023)Confrontation and Obstacle-Avoidance of Unmanned Vehicles Based on Progressive Reinforcement LearningIEEE Access10.1109/ACCESS.2023.327859711(50398-50411)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3278597
Azevedo-Sa HJayaraman SEsterwood CYang XRobert LTilbury DBelpaeme TYoung JGunes HRiek L(2020)Comparing the Effects of False Alarms and Misses on Humans' Trust in (Semi)Autonomous VehiclesCompanion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3371382.3378371(113-115)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3371382.3378371
Azevedo-Sa HYang JRobert LTilbury D(undefined)Using Trust in Automation to Enhance Driver-(Semi)Autonomous Vehicle Interaction and Improve Team PerformanceSSRN Electronic Journal10.2139/ssrn.3859704
https://doi.org/10.2139/ssrn.3859704

Index Terms

Trust In Unmanned Driving System
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. Robotic autonomy

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Published In

HRI'15 Extended Abstracts: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts

March 2015

336 pages

ISBN:9781450333184

DOI:10.1145/2701973

General Chairs:
Julie A. Adams
Vanderbilt University, USA
,
William Smart
Oregon State University, USA
,
Program Chairs:
Bilge Mutlu
University of Wisconsin-Madison, USA
,
Leila Takayama
Google[x], USA

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 March 2015

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HRI '15

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HRI '15: ACM/IEEE International Conference on Human-Robot Interaction

March 2 - 5, 2015

Oregon, Portland, USA

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HRI'15 Extended Abstracts Paper Acceptance Rate 92 of 102 submissions, 90%;

Overall Acceptance Rate 192 of 519 submissions, 37%

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Cited By

View all

Ma CLiu JHe SHong WShi J(2023)Confrontation and Obstacle-Avoidance of Unmanned Vehicles Based on Progressive Reinforcement LearningIEEE Access10.1109/ACCESS.2023.327859711(50398-50411)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3278597
Azevedo-Sa HJayaraman SEsterwood CYang XRobert LTilbury DBelpaeme TYoung JGunes HRiek L(2020)Comparing the Effects of False Alarms and Misses on Humans' Trust in (Semi)Autonomous VehiclesCompanion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3371382.3378371(113-115)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3371382.3378371
Azevedo-Sa HYang JRobert LTilbury D(undefined)Using Trust in Automation to Enhance Driver-(Semi)Autonomous Vehicle Interaction and Improve Team PerformanceSSRN Electronic Journal10.2139/ssrn.3859704
https://doi.org/10.2139/ssrn.3859704

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Recommendations

More Human-Likeness, More Trust?: The Effect of Anthropomorphism on Self-Reported and Behavioral Trust in Continued and Interdependent Human-Agent Cooperation

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