Many players in the automotive field support scenario-based assessment of automated vehicles (AVs), where individual traffic situations can be tested and, thus, facilitate concluding on the performance of AVs in different situations. Since an extremely large number of different scenarios can occur in real-world traffic, the question is how to find a finite set of relevant scenarios. Scenarios extracted from large real-world datasets represent real-world traffic since real driving data is used. Extracting scenarios, however, is challenging because (1) the scenarios to be tested should ensure the AVs behave safely, which conflicts with the fact that the majority of the data contains scenarios that are not interesting from a safety perspective, and (2) extensive data processing is required, which hinders the utilization of large real-world datasets. In this work, we propose a three-step approach for extracting scenarios from real-world driving data. The first step is data preprocessing to tackle the errors and noise in real-world data. The second step performs data tagging to label actors' activities, their interactions with each other, and their interactions with the environment. Finally, the scenarios are extracted by searching for combinations of tags. The proposed approach is evaluated using data simulated with CARLA and applied to a part of a large real-world driving dataset, i.e., the Waymo Open Motion Dataset.

翻译：汽车领域众多从业者支持基于场景的自动驾驶车辆评估方法，即通过测试单个交通场景，进而推断自动驾驶车辆在不同场景下的表现。由于真实交通中可能出现极其多样的场景，如何确定一个有限的相关系列场景便成为关键问题。从大规模真实世界数据集中提取的场景能够代表真实交通状况，因其基于实际驾驶数据。然而，场景提取面临两大挑战：（1）待测场景需确保自动驾驶车辆行为安全，这与多数数据包含非安全关键场景的实际情况相矛盾；（2）需要大量数据处理工作，这阻碍了大规模真实世界数据集的利用。本研究提出一种三阶段方法用于从真实驾驶数据中提取场景：第一阶段进行数据预处理，以消除真实数据中的误差与噪声；第二阶段执行数据标注，标记参与者行为、参与者间交互以及参与者与环境的交互；最后通过搜索标签组合来提取场景。该方法使用CARLA模拟生成的数据进行评估，并应用于大规模真实驾驶数据集（即Waymo开放运动数据集）的部分数据。