Multi-agent Trajectory Prediction with Fuzzy Query Attention

• URL: http://arxiv.org/abs/2010.15891v1
• Date: Thu, 29 Oct 2020 19:12:12 GMT
• Title: Multi-agent Trajectory Prediction with Fuzzy Query Attention
• Authors: Nitin Kamra, Hao Zhu, Dweep Trivedi, Ming Zhang, Yan Liu
• Abstract summary: Trajectory prediction for scenes with multiple agents 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.
• Score: 15.12743751614964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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