Multi-agent Trajectory Prediction with Fuzzy Query Attention
This addresses trajectory prediction for applications such as traffic and pedestrian tracking, offering a general solution with broad improvements.
The paper tackles trajectory prediction for multiple agents by modeling motion biases and interactions with a novel fuzzy query attention mechanism, achieving significant performance gains over state-of-the-art models across diverse domains like human crowds, traffic, sports, and physics datasets.
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.