  Analyzing and forecasting trajectories of agents like pedestrians and cars in
complex scenes has become more and more significant in many intelligent systems
and applications. The diversity and uncertainty in socially interactive
behaviors among a rich variety of agents make this task more challenging than
other deterministic computer vision tasks. Researchers have made a lot of
efforts to quantify the effects of these interactions on future trajectories
through different mathematical models and network structures, but this problem
has not been well solved. Inspired by marine animals that localize the
positions of their companions underwater through echoes, we build a new
anglebased trainable social interaction representation, named SocialCircle, for
continuously reflecting the context of social interactions at different angular
orientations relative to the target agent. We validate the effect of the
proposed SocialCircle by training it along with several newly released
trajectory prediction models, and experiments show that the SocialCircle not
only quantitatively improves the prediction performance, but also qualitatively
helps better simulate social interactions when forecasting pedestrian
trajectories in a way that is consistent with human intuitions.
