  Uncertainty modeling is critical in trajectory forecasting systems for both
interpretation and safety reasons. To better predict the future trajectories of
multiple agents, recent works have introduced interaction modules to capture
interactions among agents. This approach leads to correlations among the
predicted trajectories. However, the uncertainty brought by such correlations
is neglected. To fill this gap, we propose a novel concept, collaborative
uncertainty(CU), which models the uncertainty resulting from the interaction
module. We build a general CU-based framework to make a prediction model to
learn the future trajectory and the corresponding uncertainty. The CU-based
framework is integrated as a plugin module to current state-of-the-art (SOTA)
systems and deployed in two special cases based on multivariate Gaussian and
Laplace distributions. In each case, we conduct extensive experiments on two
synthetic datasets and two public, large-scale benchmarks of trajectory
forecasting. The results are promising: 1) The results of synthetic datasets
show that CU-based framework allows the model to appropriately approximate the
ground-truth distribution. 2) The results of trajectory forecasting benchmarks
demonstrate that the CU-based framework steadily helps SOTA systems improve
their performances. Especially, the proposed CU-based framework helps VectorNet
improve by 57cm regarding Final Displacement Error on nuScenes dataset. 3) The
visualization results of CU illustrate that the value of CU is highly related
to the amount of the interactive information among agents.
