Official implementation for Context-Aware Timewise VAEs for Real-Time Vehicle Trajectory Prediction.
Accepted by IEEE Robotics and Automation Letters. [arXiv][YouTube]
- Pytorch 1.11
- Numpy 1.21
We recommend to install all the requirements through Conda by
$ conda create --name <env> --file requirements.txt -c pytorch -c conda-forge
Train a model from scratch
$ python main.py \
--train <train_data_dir> --test <test_data_dir> \
--config <train_config_file> --ckpt <checkpoint_dir>
Evaluate a trained model
$ python main.py \
--test <test_data_dir> \
--config <eval_config_file> --ckpt <checkpoint_dir>
See scripts/README.md for details of data preprocessing.
We provide our configure files in config folder.
To reproduce the results, please run
# nuScenes
$ python main.py \
--train <data_dir>/train --test <data_dir>/val --map_dir <data_dir>/map \
--config config/nuscenes_train.py --ckpt <checkpoint_dir>
# Lyft
$ python main.py \
--train <data_dir>/train/0 --test <data_dir>/validate/0 --map_dir <data_dir>/map \
--config config/lyft_train.py --ckpt <checkpoint_dir> \
--rank 0 --workers 4
$ python main.py \
--train <data_dir>/train/1 --test <data_dir>/validate/1 --map_dir <data_dir>/map \
--config config/lyft_train.py --ckpt <checkpoint_dir> \
--rank 1 --workers 4 --master_addr <master_addr>
$ python main.py \
--train <data_dir>/train/2 --test <data_dir>/validate/2 --map_dir <data_dir>/map \
--config config/lyft_train.py --ckpt <checkpoint_dir> \
--rank 2 --workers 4 --master_addr <master_addr>
$ python main.py \
--train <data_dir>/train/3 --test <data_dir>/validate/3 --map_dir <data_dir>/map \
--config config/lyft_train.py --ckpt <checkpoint_dir> \
--rank 3 --workers 4 --master_addr <master_addr>
# Waymo
$ python main.py \
--train <data_dir>/training/0 --map_dir <data_dir>/map/training/0 \
--test <data_dir>/validation/0 --test_map_dir <data_dir>/map/validation/0\
--config config/waymo_train.py --ckpt <checkpoint_dir> \
--rank 0 --workers 8
...
$ python main.py \
--train <data_dir>/training/7 --map_dir <data_dir>/map/training/7 \
--test <data_dir>/validation/7 --test_map_dir <data_dir>/map/validation/7\
--config config/waymo_train.py --ckpt <checkpoint_dir> \
--rank 7 --workers 8 --master_addr <master_addr>
We use distributed training for Lyft and Waymo datasets with 4 and 8 worker machines respectively.
(cf. https://pytorch.org/docs/stable/distributed.html for Pytorch distributed training.)
All training was done with A100 GPUs for Lyft and Waymo datasets and a V100 GPU for nuScenes.
We also provided our pre-trained models in Release.
To reproduce the testing results, please run
# nuScenes
$ python main.py \
--test <data_dir>/val --map_dir <data_dir>/map \
--config config/nuscenes_eval.py --ckpt models/nuscenes_res18
# Lyft
$ python main.py \
--test <data_dir>/validate/0 --map_dir <data_dir>/map \
--config config/lyft_eval.py --ckpt models/lyft_res152 \
--rank 0 --workers 4
$ python main.py \
--test <data_dir>/validate/1 --map_dir <data_dir>/map \
--config config/lyft_eval.py --ckpt models/lyft_res152 \
--rank 1 --workers 4 --master_addr <master_addr>
$ python main.py \
--test <data_dir>/validate/2 --map_dir <data_dir>/map \
--config config/lyft_eval.py --ckpt models/lyft_res152 \
--rank 2 --workers 4 --master_addr <master_addr>
$ python main.py \
--test <data_dir>/validate/3 --map_dir <data_dir>/map \
--config config/lyft_eval.py --ckpt models/lyft_res152 \
--rank 3 --workers 4 --master_addr <master_addr>
# Waymo
$ python main.py \
--test <data_dir>/validation/0 --map_dir <data_dir>/map/validation/0 \
--config config/waymo_eval.py --ckpt models/waymo_m2 \
--rank 0 --workers 8
...
$ python main.py \
--test <data_dir>/validation/7 --map_dir <data_dir>/map/validation/7 \
--config config/waymo_eval.py --ckpt models/waymo_m2 \
--rank 7 --workers 8 --master_addr <master_addr>
@article{contextvae2023,
title={Context-Aware Timewise {VAE}s for Real-Time Vehicle Trajectory Prediction},
author={Xu, Pei and Hayet, Jean-Bernard and Karamouzas, Ioannis},
journal={IEEE Robotics and Automation Letters},
year={2023},
volume={8},
number={9},
pages={5440-5447},
doi={10.1109/LRA.2023.3295990}
}
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