  Predicting temporally consistent road users' trajectories in a multi-agent
setting is a challenging task due to unknown characteristics of agents and
their varying intentions. Besides using semantic map information and modeling
interactions, it is important to build an effective mechanism capable of
reasoning about behaviors at different levels of granularity. To this end, we
propose Dynamic goal quErieS with temporal Transductive alIgNmEnt (DESTINE)
method. Unlike past arts, our approach 1) dynamically predicts agents' goals
irrespective of particular road structures, such as lanes, allowing the method
to produce a more accurate estimation of destinations; 2) achieves map
compliant predictions by generating future trajectories in a coarse-to-fine
fashion, where the coarser predictions at a lower frame rate serve as
intermediate goals; and 3) uses an attention module designed to temporally
align predicted trajectories via masked attention. Using the common Argoverse
benchmark dataset, we show that our method achieves state-of-the-art
performance on various metrics, and further investigate the contributions of
proposed modules via comprehensive ablation studies.
