  Many players in the automotive field support scenario-based assessment of
automated vehicles (AVs), where individual traffic situations can be tested
and, thus, facilitate concluding on the performance of AVs in different
situations. Since a large number of different scenarios can occur in real-world
traffic, the question is how to find a finite set of relevant scenarios.
Scenarios extracted from large real-world datasets represent real-world traffic
since real driving data is used. Extracting scenarios, however, is challenging
because (1) the scenarios to be tested should assess the AVs behave safely,
which conflicts with the fact that the majority of the data contains scenarios
that are not interesting from a safety perspective, and (2) extensive data
processing is required, which hinders the utilization of large real-world
datasets. In this work, we propose an approach for extracting scenarios from
real-world driving data. The first step is data preprocessing to tackle the
errors and noise in real-world data by reconstructing the data. The second step
performs data tagging to label actors' activities, their interactions with each
other and the environment. Finally, the scenarios are extracted by searching
for combinations of tags. The proposed approach is evaluated using data
simulated with CARLA and applied to a part of a large real-world driving
dataset, i.e., the Waymo Open Motion Dataset (WOMD). The code and scenarios
extracted from WOMD are open to the research community to facilitate the
assessment of the automated driving functions in different scenarios.
