Surrogate Model-based Integration Testing of CPS with Complex Black Box Components

在这个项目的过程中，我主要研究了广告的场景生成。具体地说，我发现了有噪音/不确定的模拟器(如Autonomoose)的主要问题。为了克服这一困难，我开发了KNN-Average，这是一种基于搜索的算法，它改进了当前情景启发式。此外，我调查了情景生成的复杂性，并设法提出了一个层次化的情景定义框架，将这种复杂性正式化。接下来，我们与同事一起参加了两个版本的SBST CPS挑战赛，我们开发并提交了表现最好的竞争对手之一Frentic。我们正在准备一项关于情景/道路多样性及其对广告行为多样性的影响的实证研究。

In the course of the project, I primarily investigated scenario generation for ADS. Specifically, I identified a major issue with noisy/non-deterministic simulators such as Autonomoose. To overcome, I developped kNN-Averaging, a search-based algorithm that improves current scenario heuristics.Furthermore, I investigated the complexity of scenario generation and managed to proposed a hierarchical scenario definition framework, formalising the this complexity.Next, together with colleagues, we participated in two editions of the SBST CPS challenge, where we developed and submitted Frenetic, one of the top-performing competitors.We are in preparation of a empirical study of scenario/road diversity and its impact on ADS behaviour diversity.