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

Compare waymo-motion-prediction-challenge-2022-multipath-plus-plus vs M2I 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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waymo-motion-prediction-challenge-2022-multipath-plus-plus M2I
1 1
343 175
- 0.0%
2.7 0.0
about 1 year ago over 1 year ago
Python Python
GNU General Public License v3.0 or later MIT License
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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.

M2I

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

What are some alternatives?

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

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

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

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)