{"title":"Generative Edge Intelligence for IoT-Assisted Vehicle Accident Detection: Challenges and Prospects","authors":"Jiahui Liu, Yang Liu, Kun Gao, Liang Wang","doi":"10.1109/IOTM.001.2300282","DOIUrl":null,"url":null,"abstract":"With the emergence of generative intelligence at the edge of modern Internet of Things (IoT) networks, promising solutions are proposed to further improve road safety. As a crucial component of proactive traffic safety management, vehicle accident detection (VAD) encounters multiple existing challenges in terms of data accuracy, accident classification, communication latency, etc. Thus, generative edge intelligence (GEI) can be introduced to VAD systems and contribute to improving performance by augmenting data, learning underlying patterns, and so on. In this article, we investigate the integration of GEI technology in VAD systems, focusing on its applications, challenges, and prospects. We begin by reviewing conventional VAD methods and highlighting their limitations. Following this, we explore the potential of GEI in IoT-assisted VAD and then propose a novel architecture for the GEI-VAD system that is based on an end-edge-cloud framework. We delve into the details of each component and layer within the system. Finally, we conclude this article by suggesting avenues for future research.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"20 2","pages":"50-54"},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Internet of Things Magazine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IOTM.001.2300282","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With the emergence of generative intelligence at the edge of modern Internet of Things (IoT) networks, promising solutions are proposed to further improve road safety. As a crucial component of proactive traffic safety management, vehicle accident detection (VAD) encounters multiple existing challenges in terms of data accuracy, accident classification, communication latency, etc. Thus, generative edge intelligence (GEI) can be introduced to VAD systems and contribute to improving performance by augmenting data, learning underlying patterns, and so on. In this article, we investigate the integration of GEI technology in VAD systems, focusing on its applications, challenges, and prospects. We begin by reviewing conventional VAD methods and highlighting their limitations. Following this, we explore the potential of GEI in IoT-assisted VAD and then propose a novel architecture for the GEI-VAD system that is based on an end-edge-cloud framework. We delve into the details of each component and layer within the system. Finally, we conclude this article by suggesting avenues for future research.
随着生成智能在现代物联网（IoT）网络边缘的出现，人们提出了前景广阔的解决方案，以进一步改善道路安全。作为主动式交通安全管理的重要组成部分，车辆事故检测（VAD）在数据准确性、事故分类、通信延迟等方面遇到了多重挑战。因此，可以将边缘生成智能（GEI）引入 VAD 系统，通过增强数据、学习潜在模式等方式提高性能。在本文中，我们将研究如何在 VAD 系统中集成生成边缘智能技术，重点关注其应用、挑战和前景。我们首先回顾了传统的 VAD 方法，并强调了它们的局限性。随后，我们探讨了 GEI 在物联网辅助 VAD 中的潜力，然后提出了一种基于端边云框架的 GEI-VAD 系统新架构。我们深入探讨了系统中每个组件和层的细节。最后，我们提出了未来的研究方向，以此结束本文。
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