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.

翻译：场景生成是基于场景测试的关键步骤之一，因此也是驾驶员辅助功能与自动驾驶系统验证与确认的重要组成部分。然而，“场景生成”这一术语涵盖多种不同方法，例如从自然驾驶数据中提取场景或调整场景参数。本综述旨在系统梳理不同研究方法，建立场景获取与生成的不同类别，并阐明每类方法具有典型的输入与输出类型。研究表明，尽管该术语在文献中频繁使用，但各评估方法采用的输入不同，所生成场景在抽象层级和系统性视角上也存在差异。此外，本文通过近期研究及文献实例佐证了上述分类框架。