  Scenario-based methods for the assessment of Automated Vehicles (AVs) are
widely supported by many players in the automotive field. Scenarios captured
from real-world data can be used to define the scenarios for the assessment and
to estimate their relevance. Therefore, different techniques are proposed for
capturing scenarios from real-world data. In this paper, we propose a new
method to capture scenarios from real-world data using a two-step approach. The
first step consists in automatically labeling the data with tags. Second, we
mine the scenarios, represented by a combination of tags, based on the labeled
tags. One of the benefits of our approach is that the tags can be used to
identify characteristics of a scenario that are shared among different type of
scenarios. In this way, these characteristics need to be identified only once.
Furthermore, the method is not specific for one type of scenario and,
therefore, it can be applied to a large variety of scenarios. We provide two
examples to illustrate the method. This paper is concluded with some promising
future possibilities for our approach, such as automatic generation of
scenarios for the assessment of automated vehicles.
