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发展一种混合方法,为高度沉浸式环境中的运动平台产生重力度假线索。

Development of a Hybrid Method to Generate Gravito-Inertial Cues for Motion Platforms in Highly Immersive Environments.

机构信息

Institute of Robotics, Information Technologies and Communication Research (IRTIC), University of Valencia, 46980 Valencia, Spain.

Faculty of Psychology, University of Valencia, 46010 Valencia, Spain.

出版信息

Sensors (Basel). 2021 Dec 2;21(23):8079. doi: 10.3390/s21238079.

Abstract

Motion platforms have been widely used in Virtual Reality (VR) systems for decades to simulate motion in virtual environments, and they have several applications in emerging fields such as driving assistance systems, vehicle automation and road risk management. Currently, the development of new VR immersive systems faces unique challenges to respond to the user's requirements, such as introducing high-resolution 360° panoramic images and videos. With this type of visual information, it is much more complicated to apply the traditional methods of generating motion cues, since it is generally not possible to calculate the necessary corresponding motion properties that are needed to feed the motion cueing algorithms. For this reason, this paper aims to present a new method for generating non-real-time gravito-inertial cues with motion platforms using a system fed both with computer-generated-simulation-based-images and video imagery. It is a hybrid method where part of the gravito-inertial cues-those with acceleration information-are generated using a classical approach through the application of physical modeling in a VR scene utilizing washout filters, and part of the gravito-inertial cues-the ones coming from recorded images and video, without acceleration information-were generated ad hoc in a semi-manual way. The resulting motion cues generated were further modified according to the contributions of different experts based on a successive approximation-Wideband Delphi-inspired-method. The subjective evaluation of the proposed method showed that the motion signals refined with this method were significantly better than the original non-refined ones in terms of user perception. The final system, developed as part of an international road safety education campaign, could be useful for developing further VR-based applications for key fields such as driving assistance, vehicle automation and road crash prevention.

摘要

运动平台在虚拟现实 (VR) 系统中已被广泛应用数十年,用于模拟虚拟环境中的运动,并且在新兴领域如驾驶辅助系统、车辆自动化和道路风险管理方面具有多种应用。目前,新的 VR 沉浸式系统的开发面临着独特的挑战,需要满足用户的需求,例如引入高分辨率的 360°全景图像和视频。对于这种类型的视觉信息,应用传统的运动线索生成方法变得更加复杂,因为通常无法计算出为运动线索算法提供反馈所需的必要对应运动属性。出于这个原因,本文旨在提出一种使用同时接收计算机生成的基于模拟的图像和视频图像的系统来为运动平台生成非实时重力度觉线索的新方法。这是一种混合方法,其中部分重力度觉线索——具有加速度信息的那些线索——是通过在 VR 场景中应用物理建模并使用洗出滤波器来利用经典方法生成的,而部分重力度觉线索——来自记录的图像和视频的那些线索,没有加速度信息——则以半手动方式专门生成。根据不同专家的贡献,根据逐步逼近的思想——基于宽带德尔菲法的方法,对生成的运动线索进行进一步修改。根据该方法提出的运动信号的主观评估表明,与原始非精炼信号相比,用户感知方面的精炼信号显著更好。作为国际道路安全教育运动的一部分而开发的最终系统可用于为驾驶辅助、车辆自动化和道路碰撞预防等关键领域开发进一步的基于 VR 的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/baab/8659742/f9725b15d670/sensors-21-08079-g001.jpg

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