Trust Estimation for Autonomous Vehicles by Measuring Pedestrian Behavior in VR

IF 4.2 Q2 ROBOTICS

ACM Transactions on Human-Robot Interaction Pub Date : 2023-03-13 DOI:10.1145/3568294.3580072

Ryota Masuda, Shintaro Ono, T. Hiraoka, Y. Suda

引用次数: 0

Abstract

This study proposes a method to estimate pedestrian trust in an automated vehicle (AV) based on pedestrian behavior. It conducted experiments in a VR environment where an AV approached a crosswalk. Participants rated their trust in the AV at three levels before/while they crossed the road. The level can be estimated by deep learning using their skeletal coordinates, position, vehicle position, and speed during the past four seconds. The estimation accuracy was 61%.

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基于行人行为的自动驾驶车辆信任估计

本文提出了一种基于行人行为的自动驾驶车辆行人信任度评估方法。它在虚拟现实环境中进行了实验，其中自动驾驶汽车接近人行横道。参与者在过马路前/过马路时将他们对AV的信任程度分为三个等级。通过深度学习，玩家可以利用他们的骨骼坐标、位置、车辆位置和过去4秒内的速度来评估关卡。估计精度为61%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Transactions on Human-Robot Interaction Computer Science-Artificial Intelligence

CiteScore

7.70

自引率

5.90%

发文量

期刊介绍： ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain. THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.