  Modern driver assistance systems as well as autonomous vehicles take their
decisions based on local maps of the environment. These maps include, for
example, surrounding moving objects perceived by sensors as well as routes and
navigation information. Current research in the field of environment mapping is
concerned with two major challenges. The first one is the integration of
information from different sources e.g. on-board sensors like radar, camera,
ultrasound and lidar, offline map data or backend information. The second
challenge comprises in finding an abstract representation of this aggregated
information with suitable interfaces for different driving functions and
traffic situations. To overcome these challenges, an extended environment model
is a reasonable choice. In this paper, we show that role-based motion
predictions in combination with v2x-extended environment models are able to
contribute to increased traffic safety and driving comfort. Thus, we combine
the mentioned research areas and show possible improvements, using the example
of a threading process at a motorway access road. Furthermore, it is shown that
already an average v2x equipment penetration of 80% can lead to a significant
improvement of 0.33m/s^2 of the total acceleration and 12m more safety distance
compared to non v2x-equipped vehicles during the threading process.
