Real-time traffic estimation at vehicular edge nodes

Traffic estimation has been a long-studied problem, but prior work has mostly provided coarse estimates over large areas. This work proposes effective fine-grained traffic volume estimation using in-vehicle dashboard mounted cameras. Existing work on traffic estimation relies on static traffic cameras that are usually deployed at crowded intersections and at some traffic lights. For streets with no traffic cameras, some well-known navigation apps (e.g., Google Maps, Waze) are often used to get the traffic information but these applications depend on limited number of GPS traces to estimate speed, and therefore may not show the average speed experienced by every vehicle. Moreover, they do not give any information about the number of vehicles traveling on the road. In this work, we focus on harvesting vehicles as edge compute nodes, focusing on sensing and interpretation of traffic from live video streams. With this goal, we consider a system that uses the dash-cam video collected on a drive, and executes object detection and identification techniques on this data to detect and count vehicles. We use image processing techniques to estimate the lane of traveling and speed of vehicles in real-time. To evaluate this system, we recorded several trips on a major highway and a university road. The results show that vehicle count accuracy depends on traffic conditions heavily but even during the peak hours, we achieve more than 90% counting accuracy for the vehicles traveling in the left most lane. For the detected vehicles, results show that our speed estimation gives less than 10% error across diverse roads and traffic conditions, and over 91% lane estimation accuracy for vehicles traveling in the left most lane (i.e., the passing lane).