Metaverse Laboratory Hybrid Simulation Segmentation
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Solution Overview
Problem
Conventional computer simulation and modeling approaches are hindered by the challenge of accurately identifying relevant factors and inputs, coupled with the high cost and burden of computing and rendering hardware, making them inconsistent with project budgets and often failing to provide reliable and accurate simulations.
Innovation Solution
The Metaverse Laboratory (ML) is a self-contained research and development facility that integrates real, augmented, and virtual realities to create a comprehensive environment for hybrid modeling and simulation. This facility uses advanced computing and rendering hardware to support robust modeling of predetermined and dynamic scenarios, allowing for the prediction of operational changes in real-time or faster-than-real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer simulation and modeling approaches are used to achieve accurate identification of relevant factors and inputs, then simulation accuracy is improved, but computing cost and hardware burden increase significantly
Solution Approach 1:
The system segments the simulation environment into multiple virtual spaces (first virtual space, second virtual space, etc.) that can be independently rendered and processed. Each virtual space can be optimized separately, allowing accurate simulation of only the relevant factors in each segment while reducing overall computational burden.
Solution Approach 2:
The system creates virtual copies of physical objects and environments (virtual vehicle, virtual terrain, virtual obstacles) that can be manipulated and simulated without requiring full-scale physical prototypes. These virtual copies enable accurate factor identification and simulation at a fraction of the hardware cost of physical testing.
2Reliability
If comprehensive simulation factors and inputs are included to ensure reliable simulation results, then simulation reliability is improved, but project cost exceeds budget
Solution Approach 1:
The system applies different levels of simulation fidelity and detail to different regions and objects within the virtual environment. Critical areas requiring high reliability (e.g., vehicle collision zones, pedestrian interaction areas) are rendered with high detail, while less critical areas use lower detail settings, optimizing the balance between reliability and cost.
Solution Approach 2:
The system dynamically adjusts simulation parameters such as rendering resolution, physics calculation depth, and environmental detail based on the specific requirements of each simulation scenario. This allows reliable simulation results to be achieved by optimizing parameters for each case rather than maintaining maximum fidelity across all simulations, reducing overall project cost.
3Measurement precision
If multiple virtual spaces are rendered simultaneously at different time scales, then prediction accuracy for dynamic changes is improved, but computing power requirements increase
Solution Approach 1:
The system implements dynamic time scaling where different virtual spaces are rendered at different time speeds optimized for their specific purposes. Fast-time simulation spaces are used for predicting rapid dynamic changes, while slow-time spaces handle gradual environmental changes. This dynamic approach improves prediction accuracy for different types of changes while optimizing computing power utilization by avoiding uniform high-speed rendering of all spaces.
Data Source
AI summary
A Metaverse Laboratory (ML) is a self-contained comprehensive research and development (R&D) laboratory facility for hybrid modeling and simulation, conceptual and engineering design, prototyping, and experimentation in an operational environment (real, virtual, or augmented) by analyzing consequences based on simulated or actual (live) inputs from either human actors and/or predetermined scenarios. A physical facility encloses a rendering area configured to receive projected images and physical devices or objects. User interaction may be accompanied by image rendering goggles in conjunction with physical interactions with vehicles, objects and/or other users disposed in the rendering area. Computing equipment for driving a rendered scenario directs the outputs including visual and tactile feedback according to the scenario, and input from sensors and users in the rendering area determines a computed response. The collective facility provides a generalized environment for programmed realities for modeling and simulation combined with tangible objects, devices and human actors.