STGlow: A Flow-Based Generative Framework With Dual-Graphormer for Pedestrian Trajectory Prediction

The pedestrian trajectory prediction task is an essential component of intelligent systems. Its applications include but are not limited to autonomous driving, robot navigation, and anomaly detection of monitoring systems. Due to the diversity of motion behaviors and the complex social interactions among pedestrians, accurately forecasting their future trajectory is challenging. Existing approaches commonly adopt generative adversarial networks (GANs) or conditional variational autoencoders (CVAEs) to generate diverse trajectories. However, GAN-based methods do not directly model data in a latent space, which may make them fail to have full support over the underlying data distribution. CVAE-based methods optimize a lower bound on the log-likelihood of observations, which may cause the learned distribution to deviate from the underlying distribution. The above limitations make existing approaches often generate highly biased or inaccurate trajectories. In this article, we propose a novel generative flow-based framework with a dual-graphormer for pedestrian trajectory prediction (STGlow). Different from previous approaches, our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors. Besides, our method has clear physical meanings for simulating the evolution of human motion behaviors. The forward process of the flow gradually degrades complex motion behavior into simple behavior, while its reverse process represents the evolution of simple behavior into complex motion behavior. Furthermore, we introduce a dual-graphormer combined with the graph structure to more adequately model the temporal dependencies and the mutual spatial interactions. Experimental results on several benchmarks demonstrate that our method achieves much better performance compared to previous state-of-the-art approaches.

Automated summary

The pedestrian trajectory prediction task is crucial for intelligent systems and has wide applications. Existing approaches face limitations in accurately generating diverse trajectories, resulting in biased and inaccurate results. This article proposes a novel generative flow-based framework called STGlow, which optimizes the exact log-likelihood of motion behaviors to more precisely model the underlying data distribution. It also introduces a dual-graphormer combined with the graph structure to adequately model temporal dependencies and mutual spatial interactions. Experimental results demonstrate that our method outperforms previous state-of-the-art approaches.