AmeliaInference

This repository contains the inference code for the model introduced in the paper below:

Amelia: A Large Dataset and Model for Airport Surface Movement Forecasting [paper]

Ingrid Navarro *, Pablo Ortega-Kral *, Jay Patrikar *, Haichuan Wang, Zelin Ye, Jong Hoon Park, Jean Oh and Sebastian Scherer

Overview

AmeliaInference: Tool intended to be a standalone version of AmeliaTF used only for inference purposes without the training/evaluation/testing overhead code.

If you're interested in training trajectory forecasting models, please refer to AmeliaTF.

Pre-requisites

Dataset

To run this repository, you first need to download the amelia dataset. Follow the instructions here to download the dataset.

Once downloaded, create a symbolic link into datasets:

cd datasets
ln -s /path/to/amelia .

Installation

Make sure that you have conda installed.

Recommended: Use the install.sh to download and install the Amelia Framework:

chmod +x install.sh
./install.sh amelia

This will create a conda environment named amelia and install all dependencies.

Alternatively, refer to INSTALL.md for manual installation.

Note: AmeliaInference requires the Amelia dataset and AmeliaTF dependencies to run, refer to AmeliaTF's and AmeliaInference's installation.

How to use

Activate your amelia environment (Please follow the installation instructions above):

conda activate amelia

Run the testing script:

python run_inference.py -m tests=<test_name>

Where:

• <test_name> is the name of the test to run. The tests files are in a yaml format and are located in the configs/test directory. The default test is default.yaml. The test files contain the configuration for the test, including the model, the dataloader, the dataset, the device, the seed, and the output directory.

Example

The next example shows how to change the test file to example_kbos_critical.yaml, which is a test file that uses the KBOS dataset and the critical model and it is included in the repository as an example.

python run_inference.py -m tests=example_kbos_critical

Produces the following output in the output directory:

output
  |-- KBOS_26_1672610400_critical_ego
    |-- .hydra
    |-- kbos_scene_*.png
  |-- KBOS_26_1672621200_critical_ego
    |-- .hydra
    |-- kbos_scene_*.png

where:

• KBOS_26_1672610400_critical_ego and KBOS_26_1672621200_critical_ego are the directories with the specifications configured in the example_kbos_critical.yaml file.
• kbos_scene_*.png files are the images with the predictions generated by the model for each scene.

BibTeX

If you find our work useful in your research, please cite us!

@inbook{navarro2024amelia,
  author = {Ingrid Navarro and Pablo Ortega and Jay Patrikar and Haichuan Wang and Zelin Ye and Jong Hoon Park and Jean Oh and Sebastian Scherer},
  title = {AmeliaTF: A Large Model and Dataset for Airport Surface Movement Forecasting},
  booktitle = {AIAA AVIATION FORUM AND ASCEND 2024},
  chapter = {},
  pages = {},
  doi = {10.2514/6.2024-4251},
  URL = {https://arc.aiaa.org/doi/abs/10.2514/6.2024-4251},
  eprint = {https://arc.aiaa.org/doi/pdf/10.2514/6.2024-4251},
}