  In many different fields interactions between objects play a critical role in
determining their behavior. Graph neural networks (GNNs) have emerged as a
powerful tool for modeling interactions, although often at the cost of adding
considerable complexity and latency. In this paper, we consider the problem of
spatial interaction modeling in the context of predicting the motion of actors
around autonomous vehicles, and investigate alternative approaches to GNNs. We
revisit convolutions and show that they can demonstrate comparable performance
to graph networks in modeling spatial interactions with lower latency, thus
providing an effective and efficient alternative in time-critical systems.
Moreover, we propose a novel interaction loss to further improve the
interaction modeling of the considered methods.
