Waymo motion prediction challenge 2022: 3rd place solution (May, 26)
Our implementation of MultiPath++
General Info:
🏎️ CVPR2022 Workshop on Autonomous Driving website📜 Technical report🥉 Waymo Motion Prediction Challenge Website❗ Refactored code for our prize-winnig solution for Waymo Motion Prediction Challenge 2021
Team behind this solution:
Stepan Konev
Code Usage:
First we need to prepare data for training. The prerender script will convert the original data format into set of .npz files each containing the data for a single target agent. From code folder run
python3 prerender/prerender.py \
--data-path /path/to/original/data \
--output-path /output/path/to/prerendered/data \
--n-jobs 24 \
--n-shards 1 \
--shard-id 0 \
--config configs/prerender.yaml
Rendering is a memory consuming procedure so you may want to use n-shards > 1 and running the script a few times using consecutive shard-id values
Once we have our data prepared we can run the training.
python3 train.py configs/final_RoP_Cov_Single.yaml
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Citation
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@misc{https://doi.org/10.48550/arxiv.2206.10041,
doi = {10.48550/ARXIV.2206.10041},
url = {https://arxiv.org/abs/2206.10041},
author = {Konev, Stepan},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {MPA: MultiPath++ Based Architecture for Motion Prediction},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}