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5 CONCLUSIONS
In this work, we introduced a toolchain for automatic
generation of random scenarios for autonomous driv-
ing in parking sites. The scenarios are described in
OpenSCENARIO format and can be played in vari-
ous driving simulators. The toolchain was integrated
into a broader test system comprising of autonomous
vehicle control logic, vehicle dynamic, sensors, traf-
fic observer, real data database (Sch
¨
onemann et al.,
2019; Esen et al., 2020).
In our ongoing work, we are extending the catalog
of parking maneuvers (e.g. reverse and parallel bay)
and exporting the generated scenarios to the simula-
tors based on the commercial game engines.
ACKNOWLEDGEMENTS
This work has been conducted within the ENABLE-
S3 project that has received funding from the EC-
SEL Joint Undertaking under Grant Agreement no.
692455. This Joint Undertaking receives support
from the European Union’s HORIZON 2020 research
and innovation programme and Austria, Denmark,
Germany, Finland, Czech Republic, Italy, Spain, Por-
tugal, Poland, Ireland, Belgium, France, Netherlands,
United Kingdom, Slovakia, Norway.
This work was also supported by the Prystine
Project, funded by Electronic Components and Sys-
tems for European Leadership Joint Undertaking
(ECSEL JU) in collaboration with the European
Union’s H2020 Framework Programme and National
Authorities, under grant agreement no. 783190.
Tomasz Kloda was supported by the Chair for
Cyber-Physical Systems in Production Engineering at
TUM and the Alexander von Humboldt Foundation.
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