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Visual Controller Failure Detection and Mitigation

Project Website | Paper

Here is the codebase for our paper "Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers" for an aircraft taxiing problem. We present a framework for detecting system-level failures using a trained anomaly detector and implementing a fallback mechanism for safety-critical control.

Requirements

The code was tested with following setup:

  • Ubuntu 20.04
  • Python 3
  • Anaconda

Installation

git clone https://github.com/phoenixrider12/visual_failure_mitigation.git
cd visual_failure_mitigation
conda create -n xplane pip
conda activate xplane
pip install -r requirements.txt

Dataset

We prepared a dataset with varying environmental conditions using 3 Times of Day(morning, evening, and night), 2 Cloud Conditions(clear and overcast), and 5 different Runways(KMWH, KATL, PAEI, KSFO, and KEWR). We collected 20k images for each case. The total dataset size is 56GB, and you can download it using the following steps:

pip install gdown
gdown https://drive.google.com/uc?id=1ju_b36NQky_42wPzY5sLQuSNsgLISC8T
tar -zxvf taxinet_dataset.tar.gz

You should have a folder dataset containing 30 subfolders, whose names informs about TIME_OF_DAY, CLOUD_CONDITION, and AIRPORT_ID respectively, separated by underscore.

Training Anomaly Detector

Prepare the dataset (need to run only once)

python prepare_dataset.py

Train classifier

python efficientnet_training.py

The prepare dataset.py file is specific for training on our proposed training dataset consisting of 3 runways(KMWH, KATL, and PAEI), 2 times of day(morning and night), and both cloud conditions, totaling 12 cases and 240k images. For training on different cases, you can modify the prepare_dataset.py file and then train the network.

Testing Anomaly Detector

python efficientnet_inference.py

There are reconfigurable params like TIME_OF_DAY, CLOUD_CONDITION, and RUNWAY inside this file, which can be changed as per your need.

Fallback Mechanism Testing

Follow the below-mentioned steps for simulation testing of our framework.

  • Run the X-Plane simulator and choose the desired airport.
  • Run following commands in terminal
python simulate.py

You can change params like TIME_OF_DAY, CLOUD_CONDITION, RUNWAY, START_POS, and USE_FALLBACK as per your requirements.

Citation

If you find our work useful for your research, please cite:

@article{gupta2023detecting,
  title={Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers},
  author={Gupta, Aryaman and Chakraborty, Kaustav and Bansal, Somil},
  journal={arXiv preprint arXiv:2309.13475},
  year={2023}
}

Contact

Feel free to reach out to the authors for any queries regarding this project.