Huidong Gao — accepted AI-conference papers

2024

MATRIX: Multi-Agent Trajectory Generation with Diverse Contexts

Zhuo Xu, Rui Zhou, Yida Yin, Huidong Gao, Masayoshi Tomizuka, Jiachen Li

ICRA 2024poster

Data-driven methods have great advantages in modeling complicated human behavioral dynamics and dealing with many human-robot interaction applications. However, collecting massive and annotated real-world human datasets has been a laborious task, especially for highly interactive scenarios. On the o…

Cited by 6 Source ↗Scholar ↗

2022

Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting

Rui Zhou, Hongyu Zhou, Huidong Gao, Masayoshi Tomizuka, Jiachen Li, Zhuo Xu

ICRA 2022poster

Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a longstanding challenge. Recent advances in using data-driven approaches have achieved significant improvements in terms of prediction accuracy. However, the lack of group-aware analysis has limit…

Cited by 33 Source ↗Scholar ↗