Deriving HD maps for highly automated driving from vehicular probe data

High definition (HD) map data is a key feature to enable highly automated driving. With the advent of highly automated vehicles, car makers and map suppliers investigate new approaches to create and maintain HD maps by using on-board sensor data of series vehicles. While state-of-the-art-approaches focus on position and speed data analysis, the consideration of additional vehicle sensor data allows for novel approaches in the context of HD maps. By 2020, more than 30 million connected vehicles are expected to be sold per year, which will generate millions of terabytes of vehicular probe data. One of the major upcoming research issues is to find methods to exploit that probe data to generate and maintain HD maps. In this paper, we address how to develop such methods. We introduce a scalable infrastructure, which supports the ingestion, management and analysis of huge amounts of probe data. It supports an iterative process to develop, assess and tune methods for generating HD maps from probe data. We present a metric to assess methods regarding resulting map precision. As a proof of concept, we present an approach to derive road geometry of highways from location and sensor information.