Perception systems, especially cameras, are the eyes of automated driving systems. Ensuring that they function reliably and robustly is therefore an important building block in the automation of vehicles. There are various approaches to test the perception of automated driving systems. Ultimately, however, it always comes down to the investigation of the behavior of perception systems under specific input data. Camera images are a crucial part of the input data. Image data sets are therefore collected for the testing of automated driving systems, but it is non-trivial to find specific images in these data sets. Thanks to recent developments in neural networks, there are now methods for sorting the images in a data set according to their similarity to a prompt in natural language. In order to further automate the provision of search results, we make a contribution by automating the threshold definition in these sorted results and returning only the images relevant to the prompt as a result. Our focus is on preventing false positives and false negatives equally. It is also important that our method is robust and in the case that our assumptions are not fulfilled, we provide a fallback solution.

翻译：感知系统，特别是摄像头，是自动驾驶系统的“眼睛”。确保其功能可靠且鲁棒是实现车辆自动化的重要基石。测试自动驾驶系统感知的方法多种多样，但归根结底，核心始终是探究感知系统在特定输入数据下的表现。摄像头图像是输入数据的关键组成部分。因此，为测试自动驾驶系统需要收集图像数据集，但在这些数据集中寻找到特定图像并非易事。得益于神经网络的最新发展，如今已有方法可以根据图像与自然语言提示的相似度对数据集中的图像进行排序。为了进一步自动化搜索结果提供流程，本文做出贡献：我们自动设定了排序结果中的阈值，仅返回与提示相关的图像作为结果。我们的重点在于同等程度地防止假阳性和假阴性。同样重要的是，我们的方法需具备鲁棒性；当预设条件不满足时，我们提供了一种后备解决方案。