  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.
