  While there was great progress regarding the technology and its
implementation for vehicles equipped with automated driving systems (ADS), the
problem of how to proof their safety as a necessary precondition prior to
market launch remains unsolved. One promising solution are scenario-based test
approaches; however, there is no commonly accepted way of how to systematically
generate and extract the set of relevant scenarios to be tested to sufficiently
capture the real-world traffic dynamics, especially for urban operational
design domains. Within the scope of this paper, the overall concept of a novel
simulation-based toolchain for the development and testing of ADS-equipped
vehicles in urban environments is presented. Based on previous work regarding
highway environments, the developed novel enhancements aim at empowering the
toolchain to be able to deal with the increased complexity due to the more
complex road networks with multi-modal interactions of various traffic
participants. Based on derived requirements, a thorough explanation of
different modules constituting the toolchain is given, showing first results
and identified research gaps, respectively. A closer look is taken on two use
cases: First, it is investigated whether the toolchain is capable to serve as
synthetic data source within the development phase of ADS-equipped vehicles to
enrich a scenario database in terms of extent, complexity and impacts of
different what-if-scenarios for future mixed traffic. Second, it is analyzed
how to combine the individual advantages of real recorded data and an
agent-based simulation within a so-called adaptive replay-to-sim approach to
support the testing phase of an ADS-equipped vehicle. The developed toolchain
contributes to the overarching goal of a commonly accepted methodology for the
validation and safety proof of ADS-equipped vehicles, especially in urban
environments.
