XinGP/DGFNet
[RAL 2025]Multi-Agent Trajectory Prediction with Difficulty-Guided Feature Enhancement Network
This tool helps autonomous vehicle developers and robotics engineers predict the future movements of multiple objects like cars, pedestrians, or cyclists in a scene. It takes historical trajectory data of various agents as input and outputs reliable future trajectory distributions for each participant, balancing accuracy with real-time performance. This is for engineers building self-driving cars or advanced robotics systems.
No commits in the last 6 months.
Use this if you need to accurately predict the paths of multiple interacting agents in dynamic environments for applications like autonomous driving, while maintaining efficient inference speed.
Not ideal if you are looking for a tool to predict single-agent trajectories or if your application does not involve real-time multi-agent interaction forecasting.
Stars
73
Forks
7
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 24, 2025
Commits (30d)
0
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