Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation.

翻译：智能车辆与自动驾驶系统依赖场景工程实现智能与指标（I&I）、校准与认证（C&C）以及验证与确认（V&V）。在场景提取与索引过程中，各类车辆交互行为值得高度关注，并需要精细化的描述与标签。然而现有方法难以有效应对以车辆交互为核心的场景分类与标注问题。本文提出VistaScenario框架，为配备智能系统的交通自动化车辆开展交互场景工程。基于归纳的车辆交互基本类型，我们通过时空场景演化树将场景数据流切割为连续片段，并提出了基于图计算树与动态时间规整的场景度量指标Graph-DTW，用于实现精细化的场景比较与标注。极端交互场景与边缘案例可被高效过滤提取。此外，基于自然场景数据集的轨迹预测模型测试实例证明了本框架的有效性与优势。VistaScenario可为场景数据的应用与索引提供坚实支撑，进一步推动智能车辆与交通自动化技术的发展。