Utilization of Convolutional Neural Networks for Roadside Unit Placement

Intelligent transportation systems are a subset of system of systems that combine information from autonomous vehicles, road infrastructure, and other systems in order to improve the safety and efficiency of travel on roadways. The performance of an intelligent transportation system is limited by the transmission distance and delay of critical messages between components of the system of systems. Roadside units are road infrastructure designed to improve both of these limiting factors through their superior transmission capabilities and the ability to tunnel information to other roadside units over long distances. The placement of roadside units is critical to the their ability to assist the intelligent transportation system, with authors applying a number of different methods for determining the optimal positions. The goal of this work is to prove the ability of neural networks to solve this problem, which up to this point has not been attempted. A convolutional neural network is presented which uses images of the road network to determine the optimal placement of a roadside unit. The network is trained using data obtained through OpenStreetMaps, and the results demonstrate the ability of neural networks to determine the optimal placement of a roadside unit.