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Trajectory Prediction using Equivariant Continuous Convolution (ECCO)
This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivariant Continuous Convolution, by Robin Walters, Jinxi Li and Rose Yu.
Installation
This codebase is trained on Python 3.6.6+. For the usage of argoverse dataset, argoverse-api is required. We recommand the reader to follow their guide to install the complete api and datasets. Other requirements include:
- numpy>=1.18.3
- pandas>=1.0.3
- torch==1.5.0
Dependency can be installed using the following command:
pip install -r requirements.txt
Data Preparation
Original data could be downloaded from argoverse. To generate the training and validation data
- Set the path to
argoverse_forecasting
indatasets/preprocess_data.py
scripts. - Run the script
python preprocess_data.py
The data will be stored in path/to/argoverse_forecasting/train(val)/lane_data
.
Data Download
If you want to skip the data generation part, the link to preprocessed data will be provided soon.
Model Training and Evaluation
Here are commands to train the model. The evaluation will be provided after the model is trained.
For <img src="https://render.githubusercontent.com/render/math?math=\rho_1">-ECCO, run the following command
python train.py --dataset_path /path/to/argoverse_forecasting/ --rho1 --model_name rho_1_ecco --train --evaluation
For <img src="https://render.githubusercontent.com/render/math?math=\rho_{reg}">-ECCO, run the following command
python train.py --dataset_path /path/to/argoverse_forecasting/ --rho-reg --model_name rho_1_ecco --train --evaluation
For the baseline evaluation, you can refer to Argoverse Official Baseline. Note: the evaluation of the constant velocity is evaluated on the validation set (filtered out the scenes with car number greater than 60) with the velocity at final timestamp as the constant velocity.
Citation
If you find this repository useful in your research, please cite our paper:
@article{Walters2021ECCO,
title={Trajectory Prediction using Equivariant Continuous Convolution},
author={Robin Walters and Jinxi Li and Rose Yu},
journal={International Conference on Learning Representations},
year={2021},
}