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Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
[article]
2021
arXiv
pre-print
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
arXiv:2110.13947v1
fatcat:gyrsmatnovfkjfsyoeozikjthm