  Scenario generation is one of the essential steps in scenario-based testing
and, therefore, a significant part of the verification and validation of driver
assistance functions and autonomous driving systems. However, the term scenario
generation is used for many different methods, e.g., extraction of scenarios
from naturalistic driving data or variation of scenario parameters. This survey
aims to give a systematic overview of different approaches, establish different
categories of scenario acquisition and generation, and show that each group of
methods has typical input and output types. It shows that although the term is
often used throughout literature, the evaluated methods use different inputs
and the resulting scenarios differ in abstraction level and from a systematical
point of view. Additionally, recent research and literature examples are given
to underline this categorization.
