  Trajectory prediction has always been a challenging problem for autonomous
driving, since it needs to infer the latent intention from the behaviors and
interactions from traffic participants. This problem is intrinsically hard,
because each participant may behave differently under different environments
and interactions. This key is to effectively model the interlaced influence
from both spatial context and temporal context. Existing work usually encodes
these two types of context separately, which would lead to inferior modeling of
the scenarios. In this paper, we first propose a unified approach to treat time
and space dimensions equally for modeling spatio-temporal context. The proposed
module is simple and easy to implement within several lines of codes. In
contrast to existing methods which heavily rely on recurrent neural network for
temporal context and hand-crafted structure for spatial context, our method
could automatically partition the spatio-temporal space to adapt the data.
Lastly, we test our proposed framework on two recently proposed trajectory
prediction dataset ApolloScape and Argoverse. We show that the proposed method
substantially outperforms the previous state-of-the-art methods while
maintaining its simplicity. These encouraging results further validate the
superiority of our approach.
