Generative Edge Intelligence for IoT-Assisted Vehicle Accident Detection: Challenges and Prospects

作者：Jiahui Liu, Yang Liu, Kun Gao, Liang Wang · 发表于：IEEE Internet of Things Magazine · 年份：2024 · DOI：10.1109/iotm.001.2300282 · 被引用次数：13 · 研究领域：Autonomous Vehicle Technology and Safety、Traffic Prediction and Management Techniques、IoT and GPS-based Vehicle Safety Systems

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.