Multi-Agent Tensor Fusion for Contextual Trajectory Prediction

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stoch...

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Bibliographic Details
Published in:arXiv.org 2019-07
Main Authors: Zhao, Tianyang, Xu, Yifei, Monfort, Mathew, Choi, Wongun, Baker, Chris, Zhao, Yibiao, Wang, Yizhou, Ying Nian Wu
Format: Article
Language:English
Subjects:
Constraint modelling
Human behavior
Mathematical analysis
Model accuracy
Multiagent systems
Social factors
Tensors
Trajectories
Online Access:Get full text
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Summary:Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-of-the-art prediction accuracy.
ISSN:2331-8422