Learning Autoencoder Diffusion Models of Pedestrian Group Relationships for Multimodal Trajectory Prediction

Abstract

Pedestrian trajectory prediction is crucial for enabling dynamic obstacle avoidance in social robots. Variational autoencoders (VAEs) have shown potential in predicting multimodal distributions of future pedestrian trajectories. However, standards VAE struggle to generate accurate future trajectories, and existing prediction methods often overlook the relationships between pedestrian groups. This article introduces a novel prediction model, called the learning autoencoder diffusion model (LADM) of pedestrian group relationships for multimodal trajectory prediction, which takes into account pedestrian group relationships, enhancing the accuracy of multimodal distribution trajectory prediction. In the LADM framework, each pedestrian is assigned to their most probable group through a learning process, and the interaction relationships between pedestrians and groups are determined using a pedestrian–group interaction module (PGIM). To improve the quality of generated future trajectory distributions, we propose the autoencoder diffusion model (DM); the VAE functions as a generator and a DM acts as a refiner. We evaluate our proposed method on two public datasets (ETH and UCY) and compare it with state-of-the-art methods. Experimental results demonstrate that our approach outperforms existing methods in terms of average displacement error (ADE) and final displacement error (FDE) metrics.