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 a 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 assess 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 an 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 by reconstructing the data. The second step performs data tagging to label actors' activities, their interactions with each other and 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 (WOMD). The code and scenarios extracted from WOMD are open to the research community to facilitate the assessment of the automated driving functions in different scenarios.

翻译：汽车领域的众多参与者支持基于场景的自动驾驶车辆评估方法，该方法可测试单个交通情境，从而有助于判断自动驾驶车辆在不同情境下的表现。由于真实交通中可能出现大量不同场景，如何找到有限的相关场景集便成为关键问题。从大型真实数据集中提取的场景基于真实驾驶数据，能够反映真实交通状况。然而，场景提取面临两重挑战：（1）待测试场景应评估自动驾驶车辆的安全行为，这与大多数数据包含非安全视角下低价值场景的现实相矛盾；（2）需要大量数据处理，阻碍了大型真实数据集的利用。本研究提出一种从真实驾驶数据中提取场景的方法。第一步为数据预处理，通过数据重构消除真实数据中的误差与噪声；第二步进行数据标注，标记参与者行为及其相互间及其与环境的交互关系；最终通过搜索标签组合提取场景。该方法利用CARLA模拟数据进行评估，并应用于大型真实驾驶数据集的一部分——Waymo开放运动数据集（WOMD）。提取自WOMD的代码与场景已向研究社区开放，以促进不同场景下自动驾驶功能的评估。