  3D multi-object tracking (MOT) and trajectory forecasting are two critical
components in modern 3D perception systems. We hypothesize that it is
beneficial to unify both tasks under one framework to learn a shared feature
representation of agent interaction. To evaluate this hypothesis, we propose a
unified solution for 3D MOT and trajectory forecasting which also incorporates
two additional novel computational units. First, we employ a feature
interaction technique by introducing Graph Neural Networks (GNNs) to capture
the way in which multiple agents interact with one another. The GNN is able to
model complex hierarchical interactions, improve the discriminative feature
learning for MOT association, and provide socially-aware context for trajectory
forecasting. Second, we use a diversity sampling function to improve the
quality and diversity of our forecasted trajectories. The learned sampling
function is trained to efficiently extract a variety of outcomes from a
generative trajectory distribution and helps avoid the problem of generating
many duplicate trajectory samples. We show that our method achieves
state-of-the-art performance on the KITTI dataset. Our project website is at
http://www.xinshuoweng.com/projects/GNNTrkForecast.
