/counterfactual-cd-paper-resources

The code and publicly publishable data associated with a paper on counterfactual causal discovery published at IEEE IV 2023.

Primary LanguagePython

Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour

The data and code here relate to a counterfactual causal discovery framework and the experiments evaluating it, both relating to the autonomous driving domain.

Code

The code for the framework as well as any utility scripts.

Data

Data relating to the experiments carried out upon the framework. Note that the experiments referred to here rely upon the High-D dataset which is free for non-commercial use by application at the following link: https://www.highd-dataset.com/. However, as the dataset is not public, only the output experiment data is available in this repository.

Paper

Corresponding conference paper accepted at IEEE Intelligent Vehicle Symposium 2023 (Pre-print): https://arxiv.org/abs/2306.03354

Please cite the following if you use the contents of this repository in your work:

@inproceedings{howard2023simulation,
    author    = {Howard, Rhys and Kunze, Lars},
    title     = {Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour},
    booktitle = {Proceedings of the 35th IEEE Intelligent Vehicles Symposium},
    publisher = {IEEE},
    year      = {2023}
}