  Detecting pedestrians and predicting future trajectories for them are
critical tasks for numerous applications, such as autonomous driving. Previous
methods either treat the detection and prediction as separate tasks or simply
add a trajectory regression head on top of a detector. In this work, we present
a novel end-to-end two-stage network: Spatio-Temporal-Interactive Network
(STINet). In addition to 3D geometry modeling of pedestrians, we model the
temporal information for each of the pedestrians. To do so, our method predicts
both current and past locations in the first stage, so that each pedestrian can
be linked across frames and the comprehensive spatio-temporal information can
be captured in the second stage. Also, we model the interaction among objects
with an interaction graph, to gather the information among the neighboring
objects. Comprehensive experiments on the Lyft Dataset and the recently
released large-scale Waymo Open Dataset for both object detection and future
trajectory prediction validate the effectiveness of the proposed method. For
the Waymo Open Dataset, we achieve a bird-eyes-view (BEV) detection AP of 80.73
and trajectory prediction average displacement error (ADE) of 33.67cm for
pedestrians, which establish the state-of-the-art for both tasks.
