  Safe navigation of autonomous agents in human centric environments requires
the ability to understand and predict motion of neighboring pedestrians.
However, predicting pedestrian intent is a complex problem. Pedestrian motion
is governed by complex social navigation norms, is dependent on neighbors'
trajectories, and is multimodal in nature. In this work, we propose SCAN, a
Spatial Context Attentive Network that can jointly predict socially-acceptable
multiple future trajectories for all pedestrians in a scene. SCAN encodes the
influence of spatially close neighbors using a novel spatial attention
mechanism in a manner that relies on fewer assumptions, is parameter efficient,
and is more interpretable compared to state-of-the-art spatial attention
approaches. Through experiments on several datasets we demonstrate that our
approach can also quantitatively outperform state of the art trajectory
prediction methods in terms of accuracy of predicted intent.
