76 citations · 101 across the 13 of their papers we have counts for
12 papers · 1 filter
Trajectory prediction for heterogeneous agents: A performance analysis on small and imbalanced datasets
Tiago Rodrigues de Almeida, Yufei Zhu, Andrey Rudenko +4
Robots and other intelligent systems navigating in complex dynamic environments should predict future actions and intentions of surrounding agents to reach their goals efficiently…
Long-Term Human Motion Prediction Using Spatio-Temporal Maps of Dynamics
Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner +2
Long-term human motion prediction (LHMP) is important for the safe and efficient operation of autonomous robots and vehicles in environments shared with humans. Accurate prediction…
NeMo-map: Neural Implicit Flow Fields for Spatio-Temporal Motion Mapping
Yufei Zhu, Shih-Min Yang, Andrey Rudenko +3
Safe and efficient robot operation in complex human environments can benefit from good models of site-specific motion patterns. Maps of Dynamics (MoDs) provide such models by encod…
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving
Kalle Kujanpää, Daulet Baimukashev, Farzeen Munir +4
Learning to perform accurate and rich simulations of human driving behaviors from data for autonomous vehicle testing remains challenging due to human driving styles' high diversit…
THÖR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Robot Interaction
Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu +7
We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social navigation: e.g., modellin…
CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction
Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner +4
Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularl…