tedhuang96/gst
[RA-L + ICRA22] Learning Sparse Interaction Graphs of Partially Detected Pedestrians for Trajectory Prediction
This project helps roboticists and autonomous vehicle developers predict pedestrian movements more accurately. It takes in observed pedestrian trajectories, even if partially detected, and outputs predicted future paths. It's designed for engineers building navigation systems for robots or self-driving cars that need to anticipate human behavior in dynamic environments.
No commits in the last 6 months.
Use this if you need to predict the future trajectories of pedestrians, even when some individuals are only partially visible or detected in your sensor data.
Not ideal if your application requires predicting the movement of objects other than pedestrians, or if you need to predict trajectories without any historical observation data.
Stars
46
Forks
4
Language
Python
License
MIT
Category
Last pushed
Mar 19, 2022
Commits (30d)
0
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