Online Multi-Agent Forecasting with Interpretable Collaborative Graph
  Neural Network
  This paper considers predicting future statuses of multiple agents in an
online fashion by exploiting dynamic interactions in the system. We propose a
novel collaborative prediction unit (CoPU), which aggregates the predictions
from multiple collaborative predictors according to a collaborative graph. Each
collaborative predictor is trained to predict the status of an agent by
considering the impact of another agent. The edge weights of the collaborative
graph reflect the importance of each predictor. The collaborative graph is
adjusted online by multiplicative update, which can be motivated by minimizing
an explicit objective. With this objective, we also conduct regret analysis to
indicate that, along with training, our CoPU achieves similar performance with
the best individual collaborative predictor in hindsight. This theoretical
interpretability distinguishes our method from many other graph networks. To
progressively refine predictions, multiple CoPUs are stacked to form a
collaborative graph neural network. Extensive experiments are conducted on
three tasks: online simulated trajectory prediction, online human motion
prediction and online traffic speed prediction, and our methods outperform
state-of-the-art works on the three tasks by 28.6%, 17.4% and 21.0% on average,
respectively.
