Handling large amounts of data has become a key for developing automated driving systems. Especially for developing highly automated driving functions, working with images has become increasingly challenging due to the sheer size of the required data. Such data has to satisfy different requirements to be usable in machine learning-based approaches. Thus, engineers need to fully understand their large image data sets for the development and test of machine learning algorithms. However, current approaches lack automatability, are not generic and are limited in their expressiveness. Hence, this paper aims to analyze a state-of-the-art text and image embedding neural network and guides through the application in the automotive domain. This approach enables the search for similar images and the search based on a human understandable text-based description. Our experiments show the automatability and generalizability of our proposed method for handling large data sets in the automotive domain.
翻译：处理大量数据已经成为自动驾驶系统开发的关键。特别是对于开发高度自动化的驾驶功能而言，由于所需数据的纯大小，使用图像越来越具有挑战性。这样的数据必须满足不同的要求，才能在基于机器学习的方法中使用。因此，工程师需要全面理解他们的大型图像数据集，以开发和测试机器学习算法。然而，当前的方法缺少自动化，并且在表达能力上缺乏通用性。因此，本文旨在分析一种最先进的文本与图像嵌入神经网络，并指导其在汽车领域的应用。这种方法使得搜索类似图片和基于人类可理解的基于文本的描述变得可能。我们的实验表明，我们提出的方法可以自动化地处理汽车领域中的大型数据集并具有通用性。
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