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Poster
GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction
Longyuan Li · Jian Yao · Li Wenliang · Tong He · Tianjun Xiao · Junchi Yan · David Wipf · Zheng Zhang

Thu Dec 09 12:30 AM -- 02:00 AM (PST) @
in Poster Session 5 »

Learning the distribution of future trajectories conditioned on the past is a crucial problem for understanding multi-agent systems. This is challenging because humans make decisions based on complex social relations and personal intents, resulting in highly complex uncertainties over trajectories. To address this problem, we propose a conditional deep generative model that combines advances in graph neural networks. The prior and recognition model encodes two types of latent codes for each agent: an inter-agent latent code to represent social relations and an intra-agent latent code to represent agent intentions. The decoder is carefully devised to leverage the codes in a disentangled way to predict multi-modal future trajectory distribution. Specifically, a graph attention network built upon inter-agent latent code is used to learn continuous pair-wise relations, and an agent's motion is controlled by its latent intents and its observations of all other agents. Through experiments on both synthetic and real-world datasets, we show that our model outperforms previous work in multiple performance metrics. We also show that our model generates realistic multi-modal trajectories.

Keywords: Deep Learning · Generative Model · Graph Learning

Author Information

Longyuan Li (Shanghai Jiao Tong University)
Jian Yao (Fudan University)
Li Wenliang (University College London)
Tong He (Amazon Web Services)
Tianjun Xiao (Amazon)
Junchi Yan (Shanghai Jiao Tong University)
David Wipf (Microsoft Research)
Zheng Zhang (Shanghai New York Univeristy)

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