As a core technology of the autonomous driving system, pedestrian trajectory prediction can significantly enhance the function of active vehicle safety and reduce road traffic injuries. In traffic scenes, when encountering with oncoming people, pedestrians may make sudden turns or stop immediately, which often leads to complicated trajectories. To predict such unpredictable trajectories, we can gain insights into the interaction between pedestrians. In this paper, we present a novel generative method named Spatial Interaction Transformer (SIT), which learns the spatio-temporal correlation of pedestrian trajectories through attention mechanisms. Furthermore, we introduce the conditional variational autoencoder (CVAE) framework to model the future latent motion states of pedestrians. In particular, the experiments based on large-scale trafc dataset nuScenes [2] show that SIT has an outstanding performance than state-of-the-art (SOTA) methods. Experimental evaluation on the challenging ETH and UCY datasets conrms the robustness of our proposed model
翻译:作为自主驾驶系统的核心技术,行人轨道预测可以大大加强机动车辆安全功能,减少道路交通伤害。在交通场景中,当遇到行人时,行人可能会突然转弯或立即停下来,这往往导致轨道的复杂。为了预测这种无法预测的轨迹,我们可以深入了解行人之间的互动。在本文中,我们提出了一个名为空间互动变换器(SIT)的新型基因化方法,通过关注机制学习行人轨迹的轨迹与时际关系。此外,我们引入了有条件的变式自动车(CVAE)框架,以模拟行人未来潜在运动状态。特别是,基于大型变形数据元[2]的实验表明,SIT的性能优于最先进的(SOTA)方法。对具有挑战性的ET和UCY数据设置的实验性评估使我们提议的模型的强健。