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Multi-agent Trajectory Prediction with Fuzzy Query Attention
Nitin Kamra · Hao Zhu · Dweep Kumarbhai Trivedi · Ming Zhang · Yan Liu

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1239

Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning. We present a general architecture to address this challenge which models the crucial inductive biases of motion, namely, inertia, relative motion, intents and interactions. Specifically, we propose a relational model to flexibly model interactions between agents in diverse environments. Since it is well-known that human decision making is fuzzy by nature, at the core of our model lies a novel attention mechanism which models interactions by making continuous-valued (fuzzy) decisions and learning the corresponding responses. Our architecture demonstrates significant performance gains over existing state-of-the-art predictive models in diverse domains such as human crowd trajectories, US freeway traffic, NBA sports data and physics datasets. We also present ablations and augmentations to understand the decision-making process and the source of gains in our model.

Author Information

Nitin Kamra (University of Southern California)
Hao Zhu (Peking University)
Dweep Kumarbhai Trivedi (University of Southern California)
Ming Zhang (Peking University)
Yan Liu (University of Southern California)

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