A flexibly linkable meta layer of geographic features supplementary for driving automation and simulation

Abstract

In this paper, a data model named Road2Automation is introduced, aiming at supplementing road information in today's digital road maps with (georeferenced) features which are relevant to driving automation and driving simulation. It addresses maps of diverse levels of detail, precision and format (ranging from High-Definition (HD) maps to crowd-sourced data from OpenStreetMap (OSM)), and facilitates transfer of the features between maps. To this end, each feature is annotated by a logical link to its source map and by map-agnostic OpenLR references. Runtime modules for location referencing like OpenLR and a map-independent geometrical inter-map matching algorithm are needed which match georeferenced features between an arbitrary pair of source and target map. In effect, a meta layer on top of all source and target maps is realized. The meta layer addresses use cases ranging from localization, global path planning, driving simulation, and safety of autonomous transport to route planning and navigation. The model has an extendible design where new, arbitrarily complex composite keys for the logical links can be added. Existing keys or components of existing composite keys are reused, whenever new keys are constructed. On the persistence layer, this keeps the number of required database columns small.