Social-CVAE: Pedestrian Trajectory Prediction Using Conditional Variational Auto-Encoder

Pedestrian trajectory prediction is a fundamental task in applications such as autonomous driving, robot navigation, and advanced video surveillance. Since human motion behavior is inherently unpredictable, resembling a process of decision-making and intrinsic motivation, it naturally exhibits multimodality and uncertainty. Therefore, predicting multi-modal future trajectories in a reasonable manner poses challenges. The goal of multi-modal pedestrian trajectory prediction is to forecast multiple socially plausible future motion paths based on the historical motion paths of agents. In this paper, we propose a multi-modal pedestrian trajectory prediction method based on conditional variational auto-encoder. Specifically, the core of the proposed model is a conditional variational auto-encoder architecture that learns the distribution of future trajectories of agents by leveraging random latent variables conditioned on observed past trajectories. The encoder models the channel and temporal dimensions of historical agent trajectories sequentially, incorporating channel attention and self-attention to dynamically extract spatio-temporal features of observed past trajectories. The decoder is bidirectional, first estimating the future trajectory endpoints of the agents and then using the estimated trajectory endpoints as the starting position for the backward decoder to predict future trajectories from both directions, reducing cumulative errors over longer prediction ranges. The proposed model is evaluated on the widely used ETH/UCY pedestrian trajectory prediction benchmark and achieves state-of-the-art performance.