This is the codebase for the ICLR 2021 paper Trajectory Prediction using Equivariant Continuous Convolution, by Robin Walters, Jinxi Li and Rose Yu.

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

Original data could be downloaded from argoverse. To generate the training and validation data

1. Set the path to argoverse_forecasting in datasets/preprocess_data.py scripts.
2. Run the script

python preprocess_data.py

The data will be stored in path/to/argoverse_forecasting/train(val)/lane_data.

If you want to skip the data generation part, the link to preprocessed data will be provided soon.

Here are commands to train the model. The evaluation will be provided after the model is trained.

For -ECCO, run the following command

python train.py --dataset_path /path/to/argoverse_forecasting/ --rho1 --model_name rho_1_ecco --train --evaluation

For -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.

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},
}