Posterior Augmented CVAE for Pedestrian Trajectory Prediction with Momentary Observation

Pedestrian trajectory prediction aims to forecast potential movements of pedestrians given their historical observations, which plays an important role in ensuring safety of autonomous driving and robot navigation in social scenes. Plenty of works have achieved great advances when sufficient historical information is available. However, in many real-world cases in which observations are incomplete or limited due to tracking losses or sudden emergence of agents. Although recent works attempt to address this issue by designing momentary observation feature extractors or performing bi-directional prediction using diffusion to assist momentary feature learning, they are sub-optimal in prediction accuracy or computationally inefficient. In this paper, we propose a posterior augmented conditional variational autoencoder to perform momentary trajectory prediction. Specifically, we introduce unobserved historical features in the posterior encoder in CVAE, which enforces the model to utilize useful unobserved features to facilitate momentary feature learning and prediction. Moreover, reverse-enhanced multi-head attention is leveraged to make the model enhance the focus of the model on different but supplementary parts of unobserved features concerning momentary observation. This enables the model to learn more comprehensive representations, ultimately improving its ability to assist in accurate momentary predictions. Extensive experiments demonstrate that our model achieves state-of-the-art performance on ETH-UCY and Stanford Drone Datasets and our proposed module does help pedestrian trajectory prediction with momentary observations.