IEEE虚拟现实会议一直是展示虚拟现实(VR)广泛领域研究成果的主要国际场所,包括增强现实(AR),混合现实(MR)和3D用户界面中寻求高质量的原创论文。每篇论文应归类为主要涵盖研究,应用程序或系统,并使用以下准则进行分类:研究论文应描述有助于先进软件,硬件,算法,交互或人为因素发展的结果。应用论文应解释作者如何基于现有思想并将其应用到以新颖的方式解决有趣的问题。每篇论文都应包括对给定应用领域中VR/AR/MR使用成功的评估。 官网地址:http://dblp.uni-trier.de/db/conf/vr/

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Research in aviation and driving has highlighted the importance of training as an effective approach to reduce the costs associated with the supervisory role of the human in automated systems. However, only a few studies have investigated the effect of pre-trip familiarization tours on highly automated driving. In the present study, a driving simulator experiment compared the effectiveness of four familiarization groups, control, video, low fidelity virtual reality (VR), and high fidelity VR on automation trust and driving performance in several critical and non-critical transition tasks. The results revealed the positive impact of familiarization tours on trust, takeover, and handback performance at the first time of measurement. Takeover quality only improved when practice was presented in high-fidelity VR. After three times of exposure to transition requests, trust and transition performance of all groups converged to those of the high fidelity VR group, demonstrating that: a) experiencing automation failures during the training may reduce costs associated with first failures in highly automated driving; b) the VR tour with high level of interaction fidelity is superior to other types of familiarization tour, and c) uneducated and less-educated drivers learn about automation by experiencing it. Knowledge resulting from this research could help develop cost-effective familiarization tours for highly automated vehicles in dealerships and car rental centers.

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