Social Lode: Human Trajectory Prediction with Latent Odes

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.