  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.
