Interaction-Based Driving Scenario Classification and Labeling

Scenario data play a vital role in autonomous driving related researches, and it is essential to obtain refined descriptions and labels to extract and index scenarios with different types of interactions. However, existing methods cannot cope well with the problem of scenario classification and comparison with vehicle interactions as the core. In this paper, we propose a framework for interaction-based refined scenario classification and labeling. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of scenario segments via spatiotemporal scenario evolution tree. The scenario segment statistics of many published scenario datasets are further analyzed. 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 interactive scenarios and corner cases can be efficiently filtered and extracted. Moreover, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of scenario labeling and the proposed metric. The overall framework can provide solid support for the usage and indexing of scenario data.