论文标题
使用混合动力AI进行智能的交通监控
Intelligent Traffic Monitoring with Hybrid AI
论文作者
论文摘要
智能交通监控（ITMO）的挑战因大量和模式的数据以及对最先进的（SOTA）推理者的利用的需求而加剧。我们制定了ITMO的问题，并引入了Hans，这是一种用于多模式上下文理解的神经符号结构及其在ITMO中的应用。汉斯利用知识图技术作为交通域中SOTA推理的骨干。通过案例研究，我们展示了汉斯如何应对与流量监控相关的挑战，同时能够与广泛的推理方法集成
Challenges in Intelligent Traffic Monitoring (ITMo) are exacerbated by the large quantity and modalities of data and the need for the utilization of state-of-the-art (SOTA) reasoners. We formulate the problem of ITMo and introduce HANS, a neuro-symbolic architecture for multi-modal context understanding, and its application to ITMo. HANS utilizes knowledge graph technology to serve as a backbone for SOTA reasoning in the traffic domain. Through case studies, we show how HANS addresses the challenges associated with traffic monitoring while being able to integrate with a wide range of reasoning methods
