Code and data for:
Li, Z., Jiang, Q., Wu, Z., Liu, A., Wu, H., Huang, M., Huang, K. & Ku, Y. (in press). Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling. IEEE Transactions on Affective Computing.
https://doi.org/10.1109/TAFFC.2023.3279311
- A poster for Proceedings of the 45th Annual Conference of the Cognitive Science Society is available here.
- A poster for the Social & Affective Neuroscience Society (SANS) Annual Meeting 2023 is available on ResearchGate.
- A 4.2-minute video for the SANS 2023 is available on Twitter. The related slides are available on ResearchGate.
- The slides for 2022 National Doctoral Forum on Brain-Computer Intelligence and Psychology are available here.
- A poster for the 3rd Macau Symposium on Cognitive and Brain Sciences is available on ResearchGate.
- The slides for International Graduate Forum on Language Cognitive Science are available here.
- The slides for the 1st International Symposium on Addiction and Decision Making are available here.
- The slides for Greater Bay Area Young Scholar Forum on Psychological Science are available here.
- Social media: Twitter, WeChat (in Chinese), LinkedIn, Mastodon.
- arXiv preprint.
root
├── bert4keras # Adapted from https://github.com/bojone/bert4keras
├── data # Processed affective transition data & regression data & original behavioural data
│ ├── av_data
│ ├── olra_data
│ └── xls_data
├── data_prep.py # To provide functions for behavioural data processing
├── at_prep.py # To provide functions for affective transition generation
├── at_generator.py # To generate affective transition
├── sdt.py # To provide functions for model building and nested leave-one-out cross-validation
├── evaluate.py # To provide functions for model evaluation
├── fig&tbl.ipynb # To plot figures 2-8 and appendix figure 1 and make tables 2 and appendix table 1
├── tbl1_SDT-AT_Original.ipynb # Results of SDT-AT (Original) models
├── tbl1_SDT-AT_PLM-tf.ipynb # Results of SDT-AT (PLM-tf) models
├── tbl1_SDT-AT_PLM-wv.ipynb # Results of SDT-AT (PLM-wv) models
├── tbl1_Baselines # Results of Baselines models
│ ├── ml_baselines
│ └── naive_baselines.ipynb
├── appx_tbl1_olra.Rmd # Results for appendix table 1
├── teaser_image.png
├── requirements.txt
├── LICENSE
└── README.md
Note 1: to properly run all scripts, you may need to set the root of this repository as your working directory and install Python modules and packages listed in requirements.txt which I used in my MacBook Pro (M1 Max).
Note 2: to generate affective transition, you may need to download related pre-trained models or weights listed in at_generator.py.
@article{li2023Bot,
author = {Li, Zhaoning and Jiang, Qiaoli and Wu, Zhengming and Liu, Anqi and Wu, Haiyan and Huang, Miner and Huang, Kai and Ku, Yixuan},
title = {Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling}
journal = {IEEE Transactions on Affective Computing},
doi = {https://doi.org/10.1109/TAFFC.2023.3279311}
year = {2023},
}
For bug reports, please contact Zhaoning Li ([email protected], or @lizhn7).
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which gives you the right to re-use and adapt, as long as you note any changes you made, and provide a link to the original source.