A case study on optimizing the finding of infrastructure sensor positions with consideration of existing infrastructure

Abstract

Support by infrastructure sensors can be crucial to enable automated vehicles to safely navigate complex urban driving environments. Finding the suitable positions for infrastructure sensors is a complex problem with different demands and factors. This paper proposes a method of automating the process of selecting positions for infrastructure sensors in a 2D environment. The positions are selected using available data of the streets, for sensor placement suitable existing infrastructure and sensor coverage demands. This methodology is then applied to finding sensor positions in the neighborhood of Lausitzer Platz in Berlin, Germany. The sensor demands for this are to taken from a virtual roll out scenario of the U-Shift vehicle concept. This is done by first finding suitable sensor positions for the bigger streets with the highest cargo and person transportation demand and then covering of every street in the neighborhood. In this use case more than half the sensor could be placed on existing infrastructure, if there is a high density of existing infrastructure that is suitable for the placement of sensors.