Pedestrian trajectory prediction task is an essential component of
intelligent systems, and 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 the future trajectory of
pedestrians is challenging. Existing approaches commonly adopt GANs or CVAEs to
generate diverse trajectories. However, GAN-based methods do not directly model
data in a latent space, which makes them fail to have full support over the
underlying data distribution; CVAE-based methods optimize a lower bound on the
log-likelihood of observations, causing the learned distribution to deviate
from the underlying distribution. The above limitations make existing
approaches often generate highly biased or unnatural trajectories. In this
paper, we propose a novel generative flow based framework with dual graphormer
for pedestrian trajectory prediction (STGlow). Different from previous
approaches, our method can more accurately model the underlying data
distribution by optimizing the exact log-likelihood of motion behaviors.
Besides, our method has clear physical meanings to simulate the evolution of
human motion behaviors, where the forward process of the flow gradually
degrades the complex motion behavior into a simple behavior, while its reverse
process represents the evolution of a simple behavior to the complex motion
behavior. Further, we introduce a dual graphormer combining 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.Comment: 12 pages, 8 figure