This repository is built on and extends the Flow software package to explore cyber-security attacks on intelligent transportation systems. Where Flow is focused on how sparesely adopted AVs can improve traffic, this repository explores the opposite question: How can sparsely cyber-compromised AVs be used to degrade traffic flow?
Anti-Flow relies on the SUMO simulation environment. We recommend using the installation instructions here. Test the installation via the following commands:
which sumosumo --versionsumo-gui
If succesful this will print the version of sumo being run, and then open the sumo graphical user interface. It is very important that SUMO be installed correctly as the rest of Anti-Flow/Flow relies on SUMO for performing simulation.
We recommend setting up an anaconda environemnt to run Anti-Flow, which can be done using the following commands:
git clone https://github.com/georgegunter/Anti-Flow.gitcd <PATH_TO_ANTI_FLOW>conda env create -f environment.ymlconda activate anti_flowpython setup.py develop
To test whether the installation is succesfful try running:
conda activate anti_flowpython examples/simulate.py ring
This should open the sumo-gui and present the user with an ability to run a ring-road simulation environment.
For a guide on how to set up an adversarial simulation environment see \tutorials. This covers running an initial simulation, adding a compromised AV, and subseuqently using an anomaly detection technique on
Please be sure to cite not just Anti-Flow, but also Flow when using for academic reseach.
Cite Anti-Flow here:
Gunter, G., Li, H., Hojjati, A., Nice, M., Bunting, M., Gunter, C., Li, B., Sprinkle, J., & Work, D. (2021). Compromised ACC vehicles can degrade current mixed-autonomy traffic performance while remaining stealthy against detection. arXiv preprint arXiv:2112.11986.
Cite the Flow here:
Wu, C., Kreidieh, A. R., Parvate, K., Vinitsky, E., & Bayen, A. M. (2021). Flow: A modular learning framework for mixed autonomy traffic. IEEE Transactions on Robotics.