A Semantic Road Network Model for traffic simulations in virtual environments: Generation and integration

Typically, virtual traffic networks are enhanced by semantic information to simplify certain processes in traffic simulations such as navigation or decision making. In this paper, we introduce an extendable model representing road network logics (RNL) which allows the integration of such semantic information. For our specific application, we focused on the following elements: road geometry, paths leading through junctions, right of way priorities and road features. Furthermore, we describe our traffic simulation approach, which contains two layers of complexity, and show how these two layers are combined within the RNL. Since the manual setup of such RNL is time-consuming and error prone, an approach for automatically generating RNL based on standardized OpenDRIVE data is introduced. For evaluation purposes, generated RNL were integrated into 3D scenes. The mapping of the RNL to the environment was examined and simulations were run for empirical validation. As a result, the automatically generated RNL could be successfully matched with the created 3D scenes, allowing our traffic simulation approach to be run on the final scenes. Additionally, a comparison between generation times of the RNL and estimates of the time required to manually create RNL showed significant time savings, in one case of more than 300 hours.