  This paper presents online-capable deep learning model for probabilistic
vehicle trajectory prediction. We propose a simple encoder-decoder architecture
based on multi-head attention. The proposed model generates the distribution of
the predicted trajectories for multiple vehicles in parallel. Our approach to
model the interactions can learn to attend to a few influential vehicles in an
unsupervised manner, which can improve the interpretability of the network. The
experiments using naturalistic trajectories at highway show the clear
improvement in terms of positional error on both longitudinal and lateral
direction.
