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Ruohua Li

2 accepted papers

2022

Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction

RA-L 2022

Multi-pedestrian trajectory prediction is an indispensable element of autonomous systems that safely interact with crowds in unstructured environments. Many recent efforts in trajectory prediction algorithms have focused on understanding social norms behind pedestrian motions. Yet we observe these w

Cited by 29SourcecodeScholar
2021

Long-Term Pedestrian Trajectory Prediction Using Mutable Intention Filter and Warp LSTM

RA-L 2021

Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians. Critical insights from human intention and behavioral patterns need to be integrated to effectively forecast long-term pedestrian behavior. Thus, we propose a framework incorporating a m

Cited by 38SourcecodeScholar