Road State Inference via Channel State Information

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.