  Pedestrian trajectory prediction is an important technique of autonomous
driving, which has become a research hot-spot in recent years. Previous methods
mainly rely on the position relationship of pedestrians to model social
interaction, which is obviously not enough to represent the complex cases in
real situations. In addition, most of existing work usually introduce the scene
interaction module as an independent branch and embed the social interaction
features in the process of trajectory generation, rather than simultaneously
carrying out the social interaction and scene interaction, which may undermine
the rationality of trajectory prediction. In this paper, we propose one new
prediction model named Social Soft Attention Graph Convolution Network (SSAGCN)
which aims to simultaneously handle social interactions among pedestrians and
scene interactions between pedestrians and environments. In detail, when
modeling social interaction, we propose a new \emph{social soft attention
function}, which fully considers various interaction factors among pedestrians.
And it can distinguish the influence of pedestrians around the agent based on
different factors under various situations. For the physical interaction, we
propose one new \emph{sequential scene sharing mechanism}. The influence of the
scene on one agent at each moment can be shared with other neighbors through
social soft attention, therefore the influence of the scene is expanded both in
spatial and temporal dimension. With the help of these improvements, we
successfully obtain socially and physically acceptable predicted trajectories.
The experiments on public available datasets prove the effectiveness of SSAGCN
and have achieved state-of-the-art results.
