Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC). Since many modules of automated driving systems are based on machine learning (ML), CC are an essential part of the data for their development. However, there is only a limited amount of CC data in large-scale data collections, which makes them challenging in the context of ML. With a better understanding of CC, offline applications, e.g., dataset analysis, and online methods, e.g., improved performance of automated driving systems, can be improved. While there are knowledge-based descriptions and taxonomies for CC, there is little research on machine-interpretable descriptions. In this extended abstract, we will give a brief overview of the challenges and goals of such a description.

翻译：由于许多自动驾驶系统模块以机器学习为基础,因此,计算机驾驶系统是其开发数据的重要组成部分,然而,大规模数据收集中只有有限的计算机驾驶系统数据,因此在移动式数据收集中这些数据具有挑战性。 通过更好地了解计算机驾驶系统,可以改进离线应用,例如数据集分析和在线方法,例如改进自动驾驶系统的性能。虽然计算机驾驶系统有基于知识的描述和分类,但对机器解释描述的研究很少。在这种扩展的摘录中,我们将简要概述这种描述的挑战和目标。