Integrating human factors into automation process in air traffic control: intent-driven flight trajectory prediction

Introduction

This repository provides source codes of the proposed spoken instruction aware flight trajectory prediction (SIA-FTP) framework.

Repository Structure

IntentDrivenFTP
| README.md (the current README file)
| LICENSE (LICENSE file)
├─Stage1
│  └─README.md (the README file for the Stage1) 
│  
├─Stage2
│  │ BERT_PreTrain.py (traning file for the unsupervised BERT model)
│  │ config.py (configration of the models in stage2)
│  │ train.py (traning file for the IID model) 
│  │ utils.py (tools for the project)
│  │ vocab
│  │  │ intent_class.txt (dictionary of the intent and the corresponding index)
│  │  └─vocab_word.txt (dictionary of the words and the corresponding index)
│  └─data (example data for the intent-oriented instruction embedding learning)
│     └─example_data.csv (Example data file)
│ 
├─Stage3
│  │ config.json (config file using json format)
│  │ dataloader.py (load traning trajectory data)
│  │ model.py (the neural architecture corresponding to the proposed framework)
│  │ run.py (the main file for the model training and testing)
│  │ utils.py (tools for the project)
|  └─data
│     │ example_data.txt (an example of manifest)
│     └─trajectory
│           │ test1.csv (an of example trajecotry data file)
│           └─test2.csv (an of example trajecotry data file)
└─pics

Package Requirements

• Python == 3.7.1
• torch == 1.9.0 + cu110
• numpy == 1.18.5
• matplotlib == 3.2.1
• transformers == 4.10.0

System Requirements

• Ubuntu 16.04 operating system
• Intel(R) Core(TM) [email protected] CPU
• 128G of memory
• 8TB of hard disks
• 8 $\times$ NVIDIA(R) GeForce RTX(TM) 2080 Ti 11G GPUs.

Instructions

Installation

Clone this repository

git clone https://github.com/gdy-scu/IntentDrivenFTP.git

Create proper software and hardware environment

You are recommended to create a virtual environment with the package requirements mentioned above, and conduct the training and test on the suggested system configurations.

Training and Testing

In this work, a 3-stage progressive multi-modal learning paradigm is proposed to address the modality gap between the structured trajectory and unstructured spoken instructions, as well as minimize the data requirements, including trajectory-based FTP pre-training, intent-oriented instruction embedding learning, and multi-modal fine-tuning.

Stage1

Our previous work FlightBERT++ serves as the prediction framework to conduct the intent-driven FTP task. The code implementation and detailed instructions can be found in FlightBERT++.

Stage2

To pre-train the BERT model, use the following command.

cd Stage2/
python BERT_PreTrain.py

To train the IID model, use the following command.

cd Stage2/
python train.py

Stage3

The training and testing are both packaged into the script of run.py for the SIA_FTP framework with the different arguments in config.json.

The main arguments in config.json are descirbed bellow:

learning_rate: Float. The learning rate of the Adam optimizer. default=0.0001

period: Integer. The sampling period for dataloader. default=5

batch_size: Integer. The number of samples in a single training batch. default=256

epoch: Integer. The maximum epoch for training process. default=100

train_data: String. The manifest for training set. default='./data/train.csv'

dev_data: String. The manifest for validation set. default='./data/dev.csv'

test_data: String. The manifest for test set. default='./data/test.csv'

saving_dir: Integer. The save path of the models and log file during the training/testing process. default='./check_points/'

n_en_layer: Integer. The layer number of the Transformer block in the encoder. default=4

n_de_layer: Integer. The layer number of the Transformer block in the decoder. default=4

horizon: Integer. The prediction horizons of the flight trajectory prediction task. default=15

is_training: Bool. Used to specify the running mode, true for training and false for testing. default=true

use_text_emb: Bool. Integrating the spoken instruction or not. default=true

ftp_model_path: String. The checkpoint model path for trajectory-based pretraning. default=''

iid_model_path: String. The checkpoint model path for IID model. default=''

model_path: String. The checkpoint model path for SIA-FTP model. default=''

To train the SIA-FTP framework, use the following command.

cd Stage3/
python run.py --config ./config.json

Test

To test the model, set is_training to false and set the model_path to the specific test model (config.json), and run the following command.

cd Stage3/
python run.py --config ./config.json

Dataset

In this repository, the example samples are provided for facilitate quick start.

Contact

Dongyue Guo ([email protected])