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Predicting temporally consistent road users' trajectories in a multi-agentsetting is a challenging task due to unknown characteristics of agents andtheir varying intentions. Besides using semantic map information and modelinginteractions, it is important to build an effective mechanism capable ofreasoning about behaviors at different levels of granularity. To this end, wepropose Dynamic goal quErieS with temporal Transductive alIgNmEnt (DESTINE)method. Unlike past arts, our approach 1) dynamically predicts agents' goalsirrespective of particular road structures, such as lanes, allowing the methodto produce a more accurate estimation of destinations; 2) achieves mapcompliant predictions by generating future trajectories in a coarse-to-finefashion, where the coarser predictions at a lower frame rate serve asintermediate goals; and 3) uses an attention module designed to temporallyalign predicted trajectories via masked attention. Using the common Argoversebenchmark dataset, we show that our method achieves state-of-the-artperformance on various metrics, and further investigate the contributions ofproposed modules via comprehensive ablation studies.

DESTINE: Dynamic Goal Queries with Temporal Transductive Alignment for Trajectory Prediction