A Survey of the Real-Time Metaverse: Challenges and Opportunities

Future Internet, Год журнала: 2024, Номер 16(10), С. 379 - 379

Опубликована: Окт. 18, 2024

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores emerging field of technologies, which enable continuous integration dynamic, real-world data into immersive environments. We examine key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for interpretation, fast fusion algorithms, and edge computing with 5G networks low-latency transmission. reveals how these are orchestrated achieve near-instantaneous synchronization between physical worlds, defining characteristic that distinguishes its traditional counterparts. provides comprehensive insight technical challenges discusses solutions realize responsive dynamic potential applications impact across various fields considered, live entertainment, remote collaboration, simulations, urban planning digital twins. By synthesizing current research identifying future directions, foundation understanding advancing rapidly landscape contributing growing body knowledge on experiences setting stage further innovations in Metaverse transformative field.

Язык: Английский

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A MISLEADING GALLERY OF FLUID MOTION BY GENERATIVE ARTIFICIAL INTELLIGENCE

Journal of Machine Learning for Modeling and Computing, Год журнала: 2024, Номер 5(2), С. 113 - 144

Опубликована: Янв. 1, 2024

In this technical report, we extensively investigate the accuracy of outputs from well-known generative artificial intelligence (AI) applications in response to prompts describing common fluid motion phenomena familiar mechanics community. We examine a range applications, including Midjourney, Dall·E, Runway ML, Microsoft Designer, Gemini, Meta AI, and Leonardo introduced by prominent companies such as Google, OpenAI, Meta, Microsoft. Our text for generating images or videos include examples "Von Karman vortex street," "flow past an airfoil," "Kelvin-Helmholtz instability," "shock waves on sharp-nosed supersonic body," etc. compare generated these with real laboratory experiments numerical software. findings indicate that AI models are not adequately trained dynamics imagery, leading potentially misleading outputs. Beyond text-to-image/video generation, further explore transition image/video generation using tools, aiming their descriptions phenomena. This report serves cautionary note educators academic institutions, highlighting potential tools mislead students. It also aims inform researchers at renowned companies, encouraging them address issue. conjecture primary reason shortcoming is limited access copyright-protected scientific journals.

Язык: Английский

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UnstrPrompt: Large Language Model Prompt for Driving in Unstructured Scenarios

IEEE Journal of Radio Frequency Identification, Год журнала: 2024, Номер 8, С. 367 - 375

Опубликована: Янв. 1, 2024

The integration of language descriptions or prompts with Large Language Models (LLMs) into visual tasks is currently a focal point in the advancement autonomous driving. This study has showcased notable advancements across various standard datasets. Nevertheless, progress integrating faces challenges unstructured scenarios, primarily due to limited availability paired data. To address this challenge, we introduce groundbreaking prompt set called "UnstrPrompt." derived from three prominent driving datasets: IDD, ORFD, and AutoMine, collectively comprising total 6K descriptions. In response distinctive features have developed structured approach for generation, encompassing key components: scene, road, instance. Additionally, provide detailed overview generation process validation procedures. We conduct tests on segmentation tasks, our experiments demonstrated that text-image fusion can improve accuracy by more than 3% description architecture outperforms generic urban 0.1%. work holds potential advance aspects such as interaction foundational models scenario.

Язык: Английский

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Sora for Social Vision With Parallel Intelligence: Social Interaction in Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles, Год журнала: 2024, Номер 9(3), С. 4240 - 4243

Опубликована: Март 1, 2024

Artificial technologies have made rapid progress and achieved various superior tasks in the past few years, including but not limited to classification, detection, image generation data processing. Particularly, very recent emerging Sora has demonstrated exceptional ability of text-to-video lasting for 1 minute long with impressive quality. It provides a huge potential many new applications across industries, especially social interaction intelligent vehicles. The emergence innovative intelligence vehicle given rise novel requirements human-vehicle within associated contexts, where vision could play an important role. In this perspective, we present Social Interaction framework based on parallel vehicles provide perspective conducting context

Язык: Английский

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Sociolinguistic Radar of Phonological Variation and Social Meaning: Variables, Quantitative Methods, and Prospects

IEEE Transactions on Computational Social Systems, Год журнала: 2024, Номер 11(6), С. 7734 - 7741

Опубликована: Авг. 7, 2024

Inspired by the term "social radar," which collects and processes information about social behaviors, this article proposed "sociolinguistic represents an emerging branch in investigation evaluation system aiming to explore dynamic correlation between sociophonetic variants macrosociological categories, such as age, gender, ethnicity, socioeconomic status. The classic quantitative methods field include sociolinguistic surveys interview, sociophonetics, network analysis. These have been proved be effective tracking cognitive correlates of phonological variables. Under framework radar, speakers are no longer passive carriers, but active agents transforming linguistic styles process forming differentiations, thus contributing construction new meaning. With advancement neuroscience artificial intelligence (AI), neurosociolinguistic AI-based radar research will thrive empower scope strength detecting variation. working mechanism model leverages neural AI tool packages analyze variation, communication patterns, diverse phenomena. This interdisciplinary approach combines principles sociolinguistics, thoroughly examine relationship language society.

Язык: Английский

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The Emergence of the Vehicular Metaverse: A Scoping Review

Proceedings in automotive engineering, Год журнала: 2024, Номер unknown, С. 120 - 135

Опубликована: Ноя. 19, 2024

Язык: Английский

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Mobility AI Agents and Networks

IEEE Transactions on Intelligent Vehicles, Год журнала: 2024, Номер 9(7), С. 5124 - 5129

Опубликована: Июль 1, 2024

Язык: Английский

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