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arXiv · 2203.08207

SocialVAE: Human Trajectory Prediction using Timewise Latents

Pei Xu·Jean-Bernard Hayet·Ioannis Karamouzas
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Abstract

Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. However, despite significant advancements, it is still challenging for existing approaches to capture the uncertainty and multimodality of human navigation decision making. In this paper, we propose SocialVAE, a novel approach for human trajectory prediction. The core of SocialVAE is a timewise variational autoencoder architecture that exploits stochastic recurrent neural networks to perform prediction, combined with a social attention mechanism and a backward posterior approximation to allow for better extraction of pedestrian navigation strategies. We show that SocialVAE improves current state-of-the-art performance on several pedestrian trajectory prediction benchmarks, including the ETH/UCY benchmark, Stanford Drone Dataset, and SportVU NBA movement dataset. Code is available at: https://github.com/xupei0610/SocialVAE.

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Pei Xu, Jean-Bernard Hayet, Ioannis Karamouzas. 2022-03-15. SocialVAE: Human Trajectory Prediction using Timewise Latents. https://doi.org/10.1007/978-3-031-19772-7_30

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