M2I VS waymo-motion-prediction-challenge-2022-multipath-plus-plus

Compare M2I vs waymo-motion-prediction-challenge-2022-multipath-plus-plus and see what are their differences.

M2I

M2I is a simple but effective joint motion prediction framework through marginal and conditional predictions by exploiting the factorized relations between interacting agents. (by Tsinghua-MARS-Lab)
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M2I waymo-motion-prediction-challenge-2022-multipath-plus-plus
1 1
175 343
0.0% -
0.0 2.7
over 1 year ago about 1 year ago
Python Python
MIT License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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M2I

Posts with mentions or reviews of M2I. We have used some of these posts to build our list of alternatives and similar projects.

waymo-motion-prediction-challenge-2022-multipath-plus-plus

Posts with mentions or reviews of waymo-motion-prediction-challenge-2022-multipath-plus-plus. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing M2I and waymo-motion-prediction-challenge-2022-multipath-plus-plus you can also consider the following projects:

UniAD - [CVPR 2023 Best Paper] Planning-oriented Autonomous Driving

Automatic-Parking - Python implementation of an automatic parallel parking system in a virtual environment, including path planning, path tracking, and parallel parking

AgentFormer - [ICCV 2021] Official PyTorch Implementation of "AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting".

Trajformer - Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving (NeurIPS 2020)