论文标题
道路状态推论通过渠道状态信息
Road State Inference via Channel State Information
论文作者
论文摘要
最近采用了各种各样的传感器技术来进行交通监视应用程序。由于这些技术大多数都依赖有线基础架构，因此安装和维护成本限制了交通监控系统的部署。在本文中，我们介绍了一种流量监控方法，该方法利用机器学习技术处理的车辆通信中的物理层样本。我们通过广泛的模拟和现实世界实验来验证方法的可行性。首先，我们使用射线追踪模拟器和流量模拟器在现实的交通状况下模拟无线通道。接下来，我们在实际环境中进行实验，并收集从路边单元（RSU）传输的消息。结果表明，在模拟和实验数据上，我们能够以高于80％的精度来预测不同水平的服务。此外，所提出的方法能够估计两个数据中平均绝对误差较低的车辆数量。我们的方法适合与当前监视系统一起部署。它不需要对基础设施的额外投资，因为它依赖于现有的车辆网络。
A wide variety of sensor technologies are recently being adopted for traffic monitoring applications. Since most of these technologies rely on wired infrastructure, the installation and maintenance costs limit the deployment of the traffic monitoring systems. In this paper, we introduce a traffic monitoring approach that exploits physical layer samples in vehicular communications processed by machine learning techniques. We verify the feasibility of our approach with extensive simulations and real-world experiments. First, we simulate wireless channels under realistic traffic conditions using a ray-tracing simulator and a traffic simulator. Next, we conduct experiments in a real-world environment and collect messages transmitted from a roadside unit (RSU). The results show that we are able to predict different levels of service with an accuracy above 80% both on the simulation and experimental data. Further, the proposed approach is capable of estimating the number of vehicles with a low mean absolute error on both data. Our approach is suitable to be deployed alongside the current monitoring systems. It doesn't require additional investment in infrastructure since it relies on existing vehicular networks.
