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Waymo motion prediction challenge 2021: 3rd place solution

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Team behind this solution:

  1. Artsiom Sanakoyeu [Homepage] [Twitter] [Telegram Channel] [LinkedIn]
  2. Stepan Konev [LinkedIn]
  3. Kirill Brodt [GitHub]

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:

@misc{konev2022motioncnn,
      title={MotionCNN: A Strong Baseline for Motion Prediction in Autonomous Driving}, 
      author={Stepan Konev and Kirill Brodt and Artsiom Sanakoyeu},
      year={2022},
      eprint={2206.02163},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

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