Neural Network Training for Active Learning-augmented Intent-aware Obstacle Avoidance of Autonomous Surface Vehicles in High-traffic Waters Paper

• This repository contains the code for our LSTM-backbone neural network training to predict the side a vessel will pass: left or right to your vessel as well as data generator including real-world AIS / synthetic traffic.
• Submitted to IROS 2024 by Dartmouth Robotics

Contributors

• Ari Chadda
• Mingi Jeong
• Alberto Quattrini Li

Successfully Real-World Deployed on an ASV

Real-world experiment with our custom ASV catabot in operation and experimental location with 4 obstacles, where the ASV successfully navigates this scenario using the proposed method.

Tested Environment & Dependencies

• Ubuntu 20.04
• Python 3.10

For a description of Python dependencies, please see the pyproject.toml file.

Dependencies are managed using poetry. You will need to install poetry, see these instructions, we used poetry==1.6.1.

Folder structure

├── 1_preprocessing.py
├── 2_split.py
├── 3_dataset.py
├── 4_model.py
├── 5_train.py
├── 6_eval.py
├── aux_code
│   └── learning_preprocess.py
├── datasets
│   ├── extracted_data
│   ├── preprocessed_full_dataset.parquet
│   ├── preprocessed_test_dataset.parquet
│   ├── preprocessed_train_dataset.parquet
│   └── synthetic_data
├── notebooks
│   ├── AIS_based_LSTM-preprocess.ipynb
│   └── AIS-synthetic-generator.ipynb
├── poetry.lock
├── pyproject.toml
├── README.md
└── weights
    └── best.pt

Usage Instructions

To run, clone this repository and then run from the passing_intention directory:

1. Data Preprocess

In notebooks, you can run script to generate .pkl files for passing traffic ground truth data. For real-world AIS, please see the following Danish Maritime open source dataset and download .csv accordingly.

2. LSTM Training

The overall relationship of the files in this directory can be seen below.

flowchart TD
    preprocess(1_preprocess.py) --> split(2_split.py)
    split(2_split.py) --> train(5_train.py)
    dataset(3_dataset.py) --> train(5_train.py)
    model(4_model.py) --> train(5_train.py)
    train(5_train.py) --> eval(6_eval.py)

Loading

To run, please use the following commands from the root of this repository

poetry shell # create virtualenv
poetry install # install project dependencies
python 1_preprocessing.py # prepare data for training
python 2_split.py # create train/test split
python 5_train.py --parquet-path-train ./datasets/preprocessed_train_dataset.parquet --parquet-path-test ./datasets/preprocessed_test_dataset.parquet --learning-rate 1e-2 --num-workers 10 --is-training True --epochs 10000 --batch-size 20 # stopped at approx. 1000 epochs
python 6_eval.py # evaluate results

Overall Methodology

Prior State of the Art

Proposed Method

From time $t$ to $t+1$, controlled ASV, $R$'s collision avoidance behavior by state-of-the-art method vs.\ proposed method using active learning-augmented intent-awareness under an uncertain scenario where an obstacle, $O$ approaches from the left side of $R$: (\textbf{a}) At $t$, the state-of-the-art, lacking intent-awareness, predicts that O will pass on the left side (red) of $R$ and thus $R$ maintains its course as a \textit{stand-on} vessel. At $t+1$, $R$ realizes $O$ is attempting to pass on the right side (green) of $R$, resulting in a nearmiss. $R$ did a hard turn-over but it is too late. (\textbf{b}) At $t$, our method classifies the topological passing side based on the historical data from $t_h$ to $t$ and actively determines an action to increase information gain, i.e., to decrease the probability of passing on the right side (green), which is risky due to bow crossing. This proactive action with \textit{good seamanship} despite a \textit{stand-on} status leads to a safe clearance at $t+1$.