Social Lode: Human Trajectory Prediction with Latent Odes
Kexin Ke, Jian Yang, Yingjie Liu, Mingsong Chen, Xian Wei, Xuan Tang
Abstract
Human trajectory prediction is crucial in human-computer interaction and even in the safety of autonomous driving. In this work, A new method, called Social Latent Ordinary Differential Equation (Social LODE), is introduced for predicting human trajectories. The backbone of Social LODE consists of a conditional Variational Autoencoder (VAE) architecture based on Recurrent Neural Network (RNN). The hidden state updated by RNN is often discrete, but the human trajectory is continuous and uncertain. Thus, we use Latent ODEs as the decoder of VAE to overcome the limitation of RNN. Finally, we demonstrate that Social LODE achieves state-of-the-art compared to other methods, such as those involving the ETH/UCY and SDD datasets.
BibTeX
@inproceedings{icassp2024_sociallodehumant,
title = {Social Lode: Human Trajectory Prediction with Latent Odes},
author = {Kexin Ke and Jian Yang and Yingjie Liu and Mingsong Chen and Xian Wei and Xuan Tang},
booktitle = {ICASSP 2024},
year = {2024}
}