UditBhaskar19/GRAPH_NEURAL_NETWORK_FOR_RADAR_PERCEPTION
Multi-task learning using message passing graph neural network for radar based perception functions
This project helps automotive engineers and perception system developers improve how radar sensors detect and classify objects for autonomous driving. It takes raw radar point cloud measurements as input and uses deep learning to transform them, making it easier to identify distinct objects like cars, pedestrians, and large vehicles, even in complex or cluttered scenes. The output includes categorized objects, predictions for their movement, and enhanced clustering.
Use this if you are working on Advanced Driver Assistance Systems (ADAS) or autonomous driving and need to enhance the accuracy of radar-based object detection, classification, and tracking, particularly in challenging environmental conditions where traditional clustering struggles.
Not ideal if your application does not involve automotive radar perception, or if you need a simpler, rule-based object detection system where deep learning complexity is undesirable.
Stars
8
Forks
2
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 14, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/UditBhaskar19/GRAPH_NEURAL_NETWORK_FOR_RADAR_PERCEPTION"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lucidrains/rectified-flow-pytorch
Implementation of rectified flow and some of its followup research / improvements in Pytorch
probabilists/zuko
Normalizing flows in PyTorch
davidnabergoj/torchflows
Modern normalizing flows in Python. Simple to use and easily extensible.
keishihara/flow-matching
Flow Matching implemented in PyTorch
LukasRinder/normalizing-flows
Implementation of normalizing flows in TensorFlow 2 including a small tutorial.