  This paper addresses the problem of path prediction for multiple interacting
agents in a scene, which is a crucial step for many autonomous platforms such
as self-driving cars and social robots. We present \textit{SoPhie}; an
interpretable framework based on Generative Adversarial Network (GAN), which
leverages two sources of information, the path history of all the agents in a
scene, and the scene context information, using images of the scene. To predict
a future path for an agent, both physical and social information must be
leveraged. Previous work has not been successful to jointly model physical and
social interactions. Our approach blends a social attention mechanism with a
physical attention that helps the model to learn where to look in a large scene
and extract the most salient parts of the image relevant to the path. Whereas,
the social attention component aggregates information across the different
agent interactions and extracts the most important trajectory information from
the surrounding neighbors. SoPhie also takes advantage of GAN to generates more
realistic samples and to capture the uncertain nature of the future paths by
modeling its distribution. All these mechanisms enable our approach to predict
socially and physically plausible paths for the agents and to achieve
state-of-the-art performance on several different trajectory forecasting
benchmarks.
