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Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories
Pedestrian trajectory prediction is a challenging task as there are three
properties of human movement behaviors which need to be addressed, namely, the
social influence from other pedestrians, the scene constraints, and the
multimodal (multiroute) nature of predictions. Although existing methods have
explored these key properties, the prediction process of these methods is
autoregressive. This means they can only predict future locations sequentially.
In this paper, we present NAP, a non-autoregressive method for trajectory
prediction. Our method comprises specifically designed feature encoders and a
latent variable generator to handle the three properties above. It also has a
time-agnostic context generator and a time-specific context generator for
non-autoregressive prediction. Through extensive experiments that compare NAP
against several recent methods, we show that NAP has state-of-the-art
trajectory prediction performance