Predicting pedestrian motion trajectories is crucial for path planning and
motion control of autonomous vehicles. Accurately forecasting crowd
trajectories is challenging due to the uncertain nature of human motions in
different environments. For training, recent deep learning-based prediction
approaches mainly utilize information like trajectory history and interactions
between pedestrians, among others. This can limit the prediction performance
across various scenarios since the discrepancies between training datasets have
not been properly incorporated. To overcome this limitation, this paper
proposes a graph transformer structure to improve prediction performance,
capturing the differences between the various sites and scenarios contained in
the datasets. In particular, a self-attention mechanism and a domain adaption
module have been designed to improve the generalization ability of the model.
Moreover, an additional metric considering cross-dataset sequences is
introduced for training and performance evaluation purposes. The proposed
framework is validated and compared against existing methods using popular
public datasets, i.e., ETH and UCY. Experimental results demonstrate the
improved performance of our proposed scheme.

该论文提出了一个图变换器结构来改进预测性能，捕捉数据集中不同场所和情景之间的差异，并设计了一种自注意机制和域适应模块来提高模型的泛化能力。此外，还引入了一种考虑跨数据集序列的额外指标用于训练和性能评估目的。使用 ETH 和 UCY 等流行的公共数据集验证和比较了所提出的框架，实验结果表明了我们提出的方案的改进性能。