Cram3r95/mapfe4mp
Official repository for Efficient Baselines for Motion Prediction in Autonomous Driving. Presented at CVPR and ICRA Workshops 2022, and ITSC conference 2022.
This project offers efficient ways to predict where surrounding vehicles will move next, which is vital for autonomous driving systems. It takes in past vehicle trajectories and map information to output plausible future paths for multiple agents. This is for researchers and engineers developing self-driving car technology who need to optimize real-time motion prediction.
No commits in the last 6 months.
Use this if you need to develop or evaluate lightweight, high-accuracy models for predicting vehicle motion in complex autonomous driving scenarios.
Not ideal if you are looking for an out-of-the-box solution for non-autonomous driving motion prediction tasks or if your primary concern is not computational efficiency.
Stars
74
Forks
16
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 06, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Cram3r95/mapfe4mp"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
StanfordASL/Trajectron
Code accompanying "The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic...
StanfordASL/Trajectron-plus-plus
Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting...
uber-research/LaneGCN
[ECCV2020 Oral] Learning Lane Graph Representations for Motion Forecasting
agrimgupta92/sgan
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks",...
devendrachaplot/Neural-SLAM
Pytorch code for ICLR-20 Paper "Learning to Explore using Active Neural SLAM"