Ruochen Li's Outputs (2)

BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction (2025)
Journal Article
Li, R., Katsigiannis, S., Kim, T.-K., & Shum, H. P. H. (online). BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction. IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/TNNLS.2025.3545268

Trajectory prediction allows better decision-making in applications of autonomous vehicles (AVs) or surveillance by predicting the short-term future movement of traffic agents. It is classified into pedestrian or heterogeneous trajectory prediction.... Read More about BP-SGCN: Behavioral Pseudo-Label Informed Sparse Graph Convolution Network for Pedestrian and Heterogeneous Trajectory Prediction.

Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction (2025)
Journal Article
Li, R., Qiao, T., Katsigiannis, S., Zhu, Z., & Shum, H. P. (online). Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction. IEEE Transactions on Circuits and Systems for Video Technology, https://doi.org/10.1109/TCSVT.2025.3539522

Pedestrian trajectory prediction aims to forecast future movements based on historical paths. Spatial-temporal (ST) methods often separately model spatial interactions among pedestrians and temporal dependencies of individuals. They overlook the dire... Read More about Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction.