Pedestrian trajectory prediction, vital for selfdriving cars and socially-aware robots, is complicated due to intricate interactions between pedestrians, their environment, and other Vulnerable Road Users. This paper presents GSGFormer, an innovative generative model adept at predicting pedestrian trajectories by considering these complex interactions and offering a plethora of potential modal behaviors. We incorporate a heterogeneous graph neural network to capture interactions between pedestrians, semantic maps, and potential destinations. The Transformer module extracts temporal features, while our novel CVAE-Residual-GMM module promotes diverse behavioral modality generation. Through evaluations on multiple public datasets, GSGFormer not only outperforms leading methods with ample data but also remains competitive when data is limited.

翻译：行人轨迹预测对自动驾驶汽车和社交感知机器人至关重要，但由于行人之间、行人与环境及其他弱势道路使用者之间的复杂交互，这一任务具有较大难度。本文提出GSGFormer，一种创新的生成式模型，能够通过考虑这些复杂交互并提供多种潜在模态行为来有效预测行人轨迹。我们引入异构图神经网络，用于捕捉行人、语义地图与潜在目的地之间的交互关系；Transformer模块则用于提取时间特征，同时我们提出的新型CVAE-Residual-GMM模块促进了多样化行为模态的生成。通过在多个公开数据集上的评估，GSGFormer不仅在数据充足时优于主流方法，在数据有限的情况下仍保持竞争力。