@ARTICLE{Lui_Guihong_STAFGCN:A_Early,
 author={Lui, Guihong and Pan, Chenying and Zhang, Xiaoyan and Leng, Qiangkui},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e151960},
 howpublished={online},
 year={Early Access},
 abstract={Pedestrian trajectory prediction provides crucial data support for the development of smart cities. Existing pedestrian trajectory prediction methods often overlook the different types of pedestrian interactions and the micro-level spatial-temporal relationships when handling the interaction information in spatial dimension and temporal dimension. The model employs a spatial-temporal attention-based fusion graph convolutional framework to predict future pedestrian trajectories. For the different types of local and global relationships between pedestrians, it first employs spatial-temporal attention mechanisms to capture dependencies in pedestrian sequence data, obtaining the social interactions of pedestrians in spatial contexts and the movement trends of pedestrians over time. Subsequently, a fusion graph convolutional module merges the temporal weight matrix and the spatial weight matrix into a spatial-temporal fusion feature map. Finally, a decoder section utilizes TimeStacked Convolutional Neural Networks to predict future trajectories. The final validation on the ETH and UCY datasets yielded experimental results with an Average Displacement Error(ADE) of 0.34 and an Final Displacement Error(FDE) of 0.55. The visualization results further demonstrated the rationality of the model.},
 type={Article},
 title={STAFGCN:A spatial-temporal attention-based fusion graph convolution network for pedestrian trajectory prediction},
 URL={http://journals.pan.pl/Content/132812/PDF/BPASTS-04525-EA.pdf},
 doi={10.24425/bpasts.2024.151960},
 keywords={pedestrian trajectory prediction, micro-level spatial-temporal relationship, spatial-temporal attention, fusion graph convolution, Time-Stacked Convolutional Neural Network},
}