  With growing complexity and responsibility of automated driving functions in
road traffic and growing scope of their operational design domains, there is
increasing demand for covering significant parts of development, validation,
and verification via virtual environments and simulation models.
  If, however, simulations are meant not only to augment real-world
experiments, but to replace them, quantitative approaches are required that
measure to what degree and under which preconditions simulation models
adequately represent reality, and thus allow their usage for virtual testing of
driving functions. Especially in research and development areas related to the
safety impacts of the "open world", there is a significant shortage of
real-world data to parametrize and/or validate simulations - especially with
respect to the behavior of human traffic participants, whom automated vehicles
will meet in mixed traffic.
  This paper presents the intermediate results of the German AVEAS research
project (www.aveas.org) which aims at developing methods and metrics for the
harmonized, systematic, and scalable acquisition of real-world data for virtual
verification and validation of advanced driver assistance systems and automated
driving, and establishing an online database following the FAIR principles.
