A Study of Transitional Virtual Environments

Sisto, Maria; Wenk, Nicolas; Ouerhani, Nabil; Gobron, Stéphane

doi:10.1007/978-3-319-60922-5_3

Cited by 10 publications

(14 citation statements)

References 15 publications

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“…The introduction video was displayed on the front wall. We hypothesized that, at the beginning of the experiment, a simpler environment with a few visual elements could help participants better accept the system 410 [39]. The purpose of this environment was twofold.…”

Section: Light Virtual Reality Systemmentioning

confidence: 99%

Get ready for automated driving using Virtual Reality

Sportillo

Paljic

Ojeda

2018

Accident Analysis & Prevention

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In conditionally automated vehicles, drivers can engage in secondary activities while traveling to their destination. However, drivers are required to appropriately respond, in a limited amount of time, to a take-over request when the system reaches its functional boundaries. Interacting with the car in the proper way from the first ride is crucial for car and road safety in general. For this reason, it is necessary to train drivers in a risk-free environment by providing them the best practice to use these complex systems. In this context, Virtual Reality (VR) systems represent a promising training and learning tool to properly familiarize drivers with the automated vehicle and allow them to interact with the novel equipment involved. In addition, Head-Mounted Display (HMD)-based VR (light VR) would allow for the easy deployment of such training systems in driving schools or car dealerships. In this study, the effectiveness of a light Virtual Reality training program for acquiring interaction skills in automated cars was investigated. The effectiveness of this training was compared to a user manual and a fixed-base simulator with respect to both objective and self-reported measures. Sixty subjects were randomly assigned to one of the systems in which they went through a training phase followed by a test drive in a high-end driving simulator. Results show that the training system affects the take-over performances. Moreover, self-reported measures indicate that the light VR training is preferred with respect to the other systems. Finally, another important outcome of this research is the evidence that VR plays a strategic role in the definition of the set of metrics for profiling proper driver interaction with the automated vehicle.

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Section: Light Virtual Reality Systemmentioning

confidence: 99%

Get ready for automated driving using Virtual Reality

Sportillo

Paljic

Ojeda

2018

Accident Analysis & Prevention

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“…[52] -as users were focused on the task and might not have been attentive to task-irrelevant elements in the VE [53]. This is in line with a study where participants were asked to perform a dummy assembling task in an immersive VE while wearing an HMD [54]. Authors progressively changed the VE environment from a garden to an assembly workshop.…”

Section: Visual Attention and Inattentional Blindnessmentioning

confidence: 77%

Hiding Assistive Robots During Training in Immersive VR Does Not Affect Users’ Motivation, Presence, Embodiment, Performance, Nor Visual Attention

Wenk

Jordi

Buetler

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Combining immersive virtual reality (VR) using head-mounted displays (HMDs) with assisting robotic devices might be a promising procedure to enhance neurorehabilitation. However, it is still an open question how immersive virtual environments (VE) should be designed when interacting with rehabilitation robots. In conventional training, the robot is usually not visually represented in the VE, resulting in a visuo-haptic sensory conflict between what users see and feel. This study aimed to investigate how motivation, embodiment, and presence are affected by this visuo-haptic sensory conflict. Using an HMD and a rehabilitation robot, 28 healthy participants performed a path-tracing task, while the robot was either visually reproduced in the VE or not and while the robot either assisted the movements or not. Participants' performance and visual attention were measured during the tasks, and after each visibility/assistance condition, they reported their motivation, presence, and embodiment with questionnaires. We found that, independently of the assistance, the robot visibility did not affect participants' motivation, presence, embodiment, nor task performance. We only found a greater effort/importance reported when the robot was visible. The visual attention was also slightly affected by the robot's visibility. Importantly, we found that the robotic assistance hampered presence and embodiment, but improved motivation. Our results indicate no disadvantage of not reproducing robotic devices in VEs when using HMDs. However, caution must be put when developing assisting controllers, as they might hamper users' affect.

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“…In the best case, the user does not notice much, since only objects currently invisible to the user are transitioned. Such a technique is implemented by Sisto et al [SWOG17]. The HMD-based VR application by Valkov and Flagge [VF17] is using the offscreen transition to switch from a replica of the real environment to the actual target environment.…”

Section: Offscreen Transitionmentioning

confidence: 99%

“…For these quantitative comparisons, ten papers used objective measures, such as task times and completion rates, and all 15 included subjective data from self-assessment questionnaires, such as standardized or custom questionnaires on task load, presence, immersion or similar (more details in Section 8.3). Unlike the 15 papers mentioned above, Sisto et al [SWOG17] did not compare different conditions but rather evaluated whether participants noticed changes in the environment while being involved in a task unrelated to the environmental changes. In addition to a quantitative approach, five papers used qualitative methods, such as semi-structured [NJ19, PDE*19] or unstructured interviews [ESE06,GLB05] as well as the think-aloud method [CDH*19b, ESE06], to gather feedback on the participant's experience.…”

Section: Study Designsmentioning

confidence: 99%

A Survey on Cross‐Virtuality Analytics

Fröhler

Anthes

Pointecker

et al. 2022

Computer Graphics Forum

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Cross‐virtuality analytics (XVA) is a novel field of research within immersive analytics and visual analytics. A broad range of heterogeneous devices across the reality–virtuality continuum, along with respective visual metaphors and analysis techniques, are currently becoming available. The goal of XVA is to enable visual analytics that use transitional and collaborative interfaces to seamlessly integrate different devices and support multiple users. In this work, we take a closer look at XVA and analyse the existing body of work for an overview of its current state. We classify the related literature regarding ways of establishing cross‐virtuality by interconnecting different stages in the reality–virtuality continuum, as well as techniques for transitioning and collaborating between the different stages. We provide insights into visualization and interaction techniques employed in current XVA systems. We report on ways of evaluating such systems, and analyse the domains where such systems are becoming available. Finally, we discuss open challenges in XVA, giving directions for future research.

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A Study of Transitional Virtual Environments

Cited by 10 publications

References 15 publications

Get ready for automated driving using Virtual Reality

Get ready for automated driving using Virtual Reality

Hiding Assistive Robots During Training in Immersive VR Does Not Affect Users’ Motivation, Presence, Embodiment, Performance, Nor Visual Attention

A Survey on Cross‐Virtuality Analytics

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