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：处理海量数据已成为开发自动驾驶系统的关键。特别是在开发高级自动驾驶功能时，由于所需数据规模庞大，处理图像信息变得愈发具有挑战性。此类数据需满足不同要求，方可应用于基于机器学习的方法。因此，工程师必须充分理解其庞大的图像数据集，以开发和测试机器学习算法。然而，当前方法缺乏自动化能力、不具备通用性，且表达能力有限。为此，本文旨在分析一种先进的文本与图像嵌入神经网络，并引导其在汽车领域中的应用。该方法支持基于人类可理解的文本描述进行相似图像搜索及目标检索。实验结果表明，我们提出的方法在处理汽车领域大规模数据时具有自动化与通用性。