waymo-motion-prediction-2021
Waymo motion prediction challenge 2021: 3rd place solution.
Dataset
Download
datasets
uncompressed/tf_example/{training,validation,testing}
Prerender
Change paths to input dataset and output folders
python prerender.py \
--data /home/data/waymo/training \
--out ./train
python prerender.py \
--data /home/data/waymo/validation \
--out ./dev \
--use-vectorize \
--n-shards 1
python prerender.py \
--data /home/data/waymo/testing \
--out ./test \
--use-vectorize \
--n-shards 1
Training
MODEL_NAME=xception71
python train.py \
--train-data ./train \
--dev-data ./dev \
--save ./${MODEL_NAME} \
--model ${MODEL_NAME} \
--img-res 224 \
--in-channels 25 \
--time-limit 80 \
--n-traj 6 \
--lr 0.001 \
--batch-size 48 \
--n-epochs 120
Submit
python submit.py \
--test-data ./test/ \
--model-path ${MODEL_PATH_TO_JIT} \
--save ${SAVE}
Visualize predictions
python visualize.py \
--model ${MODEL_PATH_TO_JIT} \
--data ${DATA_PATH} \
--save ./viz
Citation
If you find our work useful, please cite it as:
@article{konev2021motioncnn,
title={MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving},
author={Konev, Stepan and Brodt, Kirill and Sanakoyeu, Artsiom},
year={2021}
}