ksm26/OccupancyGrid-Predictions
Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning
This project helps self-driving car engineers predict the future layout of an urban environment. It takes past visual data (occupancy grids showing where vehicles and static objects are) and uses it to generate predictions of where everything will be up to 3 seconds in the future. This allows autonomous navigation systems to make safer and more informed decisions.
No commits in the last 6 months.
Use this if you are developing autonomous vehicles and need to forecast dynamic road conditions, especially the movement of other vehicles, without relying on high-definition maps.
Not ideal if your application doesn't involve predicting spatial occupancy in highly dynamic, real-world traffic scenarios for autonomous navigation.
Stars
38
Forks
4
Language
Python
License
MIT
Category
Last pushed
Jul 04, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ksm26/OccupancyGrid-Predictions"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
AndreiBarsan/DynSLAM
Master's Thesis on Simultaneous Localization and Mapping in dynamic environments. Separately...
gradslam/gradslam
gradslam is an open source differentiable dense SLAM library for PyTorch
jbwang1997/OPUS
OPUS: Occupancy Prediction Using a Sparse Set
ai4ce/DiscoNet
[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception
mchancan/deepseqslam
The Official Deep Learning Framework for Robot Place Learning