Trajectory prediction allows better decision-making in applications of autonomous vehicles or surveillance by predicting the short-term future movement of traffic agents. It is classified into pedestrian or heterogeneous trajectory prediction. The former exploits the relatively consistent behavior of pedestrians, but is limited in real-world scenarios with heterogeneous traffic agents such as cyclists and vehicles. The latter typically relies on extra class label information to distinguish the heterogeneous agents, but such labels are costly to annotate and cannot be generalized to represent different behaviors within the same class of agents. In this work, we introduce the behavioral pseudo-labels that effectively capture the behavior distributions of pedestrians and heterogeneous agents solely based on their motion features, significantly improving the accuracy of trajectory prediction. To implement the framework, we propose the Behavioral Pseudo-Label Informed Sparse Graph Convolution Network (BP-SGCN) that learns pseudo-labels and informs to a trajectory predictor. For optimization, we propose a cascaded training scheme, in which we first learn the pseudo-labels in an unsupervised manner, and then perform end-to-end fine-tuning on the labels in the direction of increasing the trajectory prediction accuracy. Experiments show that our pseudo-labels effectively model different behavior clusters and improve trajectory prediction. Our proposed BP-SGCN outperforms existing methods using both pedestrian (ETH/UCY, pedestrian-only SDD) and heterogeneous agent datasets (SDD, Argoverse 1).

翻译：轨迹预测通过预测交通参与者的短期未来运动，能够在自动驾驶或监控应用中实现更优的决策。该任务可分为行人轨迹预测与异构轨迹预测两类。前者利用行人行为相对一致的特点，但在包含骑行者、车辆等异构交通参与者的真实场景中受限。后者通常依赖额外的类别标签信息来区分异构参与者，但此类标签标注成本高昂，且无法泛化表示同一类别参与者内部的不同行为。本文提出行为伪标签，仅基于运动特征即可有效捕捉行人及异构参与者的行为分布，显著提升轨迹预测的准确性。为实现该框架，我们提出行为伪标签引导的稀疏图卷积网络（BP-SGCN），该网络学习伪标签并将其信息传递至轨迹预测器。在优化方面，我们提出一种级联训练策略：首先以无监督方式学习伪标签，随后沿提升轨迹预测精度的方向对标签进行端到端微调。实验表明，我们的伪标签能有效建模不同行为簇并改善轨迹预测性能。所提出的BP-SGCN在行人数据集（ETH/UCY、纯行人SDD）与异构参与者数据集（SDD、Argoverse 1）上均优于现有方法。