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 GANs or 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 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 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. 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.

翻译：行人轨迹预测任务是智能系统的重要组成部分，其应用包括但不限于自动驾驶、机器人导航和监控系统异常检测。由于运动行为的多样性以及行人之间的复杂社会交互，准确预测其未来轨迹具有挑战性。现有方法通常采用生成对抗网络（GANs）或条件变分自编码器（CVAEs）来生成多样化轨迹。然而，基于GAN的方法无法直接在潜在空间中对数据进行建模，可能导致其对底层数据分布的支持不完整；基于CVAE的方法优化观测对数似然的变分下界，可能使学习到的分布偏离真实分布。上述局限性导致现有方法常生成高度偏离或不准确的轨迹。本文提出一种新颖的基于流生成框架与双Graphormer的行人轨迹预测方法（STGlow）。与现有方法不同，本方法通过精确优化运动行为的对数似然，能更准确地建模底层数据分布。此外，本方法在模拟人类运动行为演化方面具有清晰的物理含义：流的正向过程将复杂运动行为逐步退化为简单行为，而逆向过程则表征简单行为向复杂运动行为的演化。进一步，我们引入结合图结构的双Graphormer，以更充分地建模时间依赖性和空间交互作用。在多个基准数据集上的实验结果表明，与现有最先进方法相比，本方法取得了显著更优的性能。