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Collaborative Uncertainty in Multi-Agent Trajectory Forecasting
Bohan Tang · Yiqi Zhong · Ulrich Neumann · Gang Wang · Siheng Chen · Ya Zhang

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @

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 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 nicely rebuild the ground-truth distribution. 2) The results of trajectory forecasting benchmarks demonstrate that the CU-based framework steadily helps SOTA systems improve their performances. Specially, the proposed CU-based framework helps VectorNet improve by 57 cm 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.

Author Information

Bohan Tang (University of Oxford)
Yiqi Zhong (University of Southern California)
Ulrich Neumann (USC)
Gang Wang (Beijing Institute of Technology)

Gang Wang received the B.Eng. degree in Electrical Engineering and Automation from the Beijing Institute of Technology, Beijing, China, in 2011. He is currently a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Minnesota. His research interests focus on the areas of high-dimensional statistical learning, and nonconvex optimization, and deep learning. He received the National Scholarship from China in 2014, the Student Travel Grant from the Signal Processing Community in 2016, and a Best Student Paper Award at the 2017 European Signal Processing Conference.

Siheng Chen (MERL)
Ya Zhang (Cooperative Medianet Innovation Center, Shang hai Jiao Tong University)

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