STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction
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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.
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