One core challenge in the development of automated vehicles is their capability to deal with a multitude of complex trafficscenarios with many, hard to predict traffic participants. As part of the iterative development process, it is necessary to detect criticalscenarios and generate knowledge from them to improve the highly automated driving (HAD) function. In order to tackle this challenge,numerous datasets have been released in the past years, which act as the basis for the development and testing of such algorithms.Nevertheless, the remaining challenges are to find relevant scenes, such as safety-critical corner cases, in these datasets and tounderstand them completely.Therefore, this paper presents a methodology to process and analyze naturalistic motion datasets in two ways: On the one hand, ourapproach maps scenes of the datasets to a generic semantic scene graph which allows for a high-level and objective analysis. Here,arbitrary criticality measures, e.g. TTC, RSS or SFF, can be set to automatically detect critical scenarios between traffic participants.On the other hand, the scenarios are recreated in a realistic virtual reality (VR) environment, which allows for a subjective close-upanalysis from multiple, interactive perspectives.

中文翻译：

---

简化数据集：将道路用户交互的可视化与虚拟现实中的场景重构相结合

自动驾驶汽车发展中的一项核心挑战是其应对多种复杂交通场景的能力，其中涉及许多难以预测的交通参与者。作为迭代开发过程的一部分，有必要检测关键场景并从中获取知识，以改善高度自动驾驶（HAD）功能。为了应对这一挑战，过去几年中发布了许多数据集，这些数据集是开发和测试此类算法的基础。尽管如此，剩下的挑战还是要找到相关的场景，例如对安全至关重要的极端案例，因此，本文提出了一种以两种方式处理和分析自然运动数据集的方法：一方面，我们的方法将数据集的场景映射到通用的语义场景图，从而可以进行高层和客观的分析。在这里，可以设置任意临界度，例如TTC，RSS或SFF，以自动检测交通参与者之间的关键场景。另一方面，这些场景是在现实的虚拟现实（VR）环境中重新创建的，从而可以实现主观的关闭从多个互动角度进行分析。