A scenario-based testing approach can reduce the time required to obtain statistically significant evidence of the safety of Automated Driving Systems (ADS). Identifying these scenarios in an automated manner is a challenging task. Most methods on scenario classification do not work for complex scenarios with diverse environments (highways, urban) and interaction with other traffic agents. This is mirrored in their approaches which model an individual vehicle in relation to its environment, but neglect the interaction between multiple vehicles (e.g. cut-ins, stationary lead vehicle). Furthermore, existing datasets lack diversity and do not have per-frame annotations to accurately learn the start and end time of a scenario. We propose a method for complex traffic scenario classification that is able to model the interaction of a vehicle with the environment, as well as other agents. We use Graph Convolutional Networks to model spatial and temporal aspects of these scenarios. Expanding the nuScenes and Argoverse 2 driving datasets, we introduce a scenario-labeled dataset, which covers different driving environments and is annotated per frame. Training our method on this dataset, we present a promising baseline for future research on per-frame complex scenario classification.

翻译：基于场景的测试方法可以减少获取自动驾驶系统安全性的统计显著证据所需的时间。然而，以自动化方式识别这些场景是一项具有挑战性的任务。现有的大多数场景分类方法无法应对包含多样环境（高速公路、城市）以及与其他交通参与者交互的复杂场景。这体现在它们的方法仅建模单个车辆与其环境的关系，而忽略了多车辆之间的交互（例如，切入、前方静止车辆）。此外，现有数据集缺乏多样性，且没有逐帧标注来准确学习场景的起始和结束时间。我们提出了一种用于复杂交通场景分类的方法，该方法能够建模车辆与环境以及其他交通参与者之间的交互。我们使用图卷积网络对场景的时空方面进行建模。通过扩展nuScenes和Argoverse 2驾驶数据集，我们引入了一个覆盖不同驾驶环境并逐帧标注的场景标注数据集。在该数据集上训练我们的方法，我们为未来逐帧复杂场景分类研究提供了一个有前景的基线。