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Autonomous vehicles (AVs) need to share the road with multiple, heterogeneousroad users in a variety of driving scenarios. It is overwhelming andunnecessary to carefully interact with all observed agents, and AVs need todetermine whether and when to interact with each surrounding agent. In order tofacilitate the design and testing of prediction and planning modules of AVs,in-depth understanding of interactive behavior is expected with properrepresentation, and events in behavior data need to be extracted andcategorized automatically. Answers to what are the essential patterns ofinteractions are also crucial for these motivations in addition to answeringwhether and when. Thus, learning to extract interactive driving events andpatterns from human data for tackling the whether-when-what tasks is ofcritical importance for AVs. There is, however, no clear definition andtaxonomy of interactive behavior, and most of the existing works are based oneither manual labelling or hand-crafted rules and features. In this paper, wepropose the Interactive Driving event and pattern Extraction Network (IDE-Net),which is a deep learning framework to automatically extract interaction eventsand patterns directly from vehicle trajectories. In IDE-Net, we leverage thepower of multi-task learning and proposed three auxiliary tasks to assist thepattern extraction in an unsupervised fashion. We also design a uniquespatial-temporal block to encode the trajectory data. Experimental results onthe INTERACTION dataset verified the effectiveness of such designs in terms ofbetter generalizability and effective pattern extraction. We find threeinterpretable patterns of interactions, bringing insights for driver behaviorrepresentation, modeling and comprehension. Both objective and subjectiveevaluation metrics are adopted in our analysis of the learned patterns.

IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data