Ying-Fan Huang

University of Chinese Academy of Sciences

Papers

Total Citations

634

H-Index

About

Ying-Fan Huang is a leading researcher in human trajectory prediction, a field critical to the safe operation of autonomous vehicles and social robots. His most influential work, "STGAT: Modeling Spatial-Temporal Interactions for Human Trajectory Prediction" (2019), has garnered over 634 citations, establishing him as a key figure in this domain. Huang’s major contribution lies in his innovative modeling of the complex interplay between spatial and temporal interactions among pedestrians. Recognizing that movement in crowded spaces is both continuous and anticipatory, his research demonstrates how individuals simultaneously consider their immediate spatial surroundings and their past motion patterns to avoid collisions. This dual-focus approach has significantly advanced the accuracy and realism of predictive models. Beyond this landmark paper, Huang’s body of work consistently pushes the boundaries of understanding social dynamics in motion, providing foundational insights for safer, more intelligent autonomous systems. His research is essential reading for students and engineers working at the intersection of computer vision, robotics, and multi-agent systems.