CHI 2023 paper – Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving

This page contains our feature engineering pipeline source code for our manuscript submitted to CHI 2023:

Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving

Content

This repo consists of three major parts: (i) a command line tool to record the eye tracking data from a Tobii Nano Pro, (ii) a tool to calculate eye event data, and (iii) a tool to calculate features from the eye tracking data. We will describe in the following on how to get started with this code in more detail.

Prerequisites: We recommend to use Python 3.8 and to install dependencies via pip install -U -r requirements.txt

• tobii_nano_pro_recorder: A dedicated README file in the folder explains on how to use the command line tool to record Tobii Nano Pro data.
• eye_event_classification: We use the REMoDNaV algorithm to annotate the collected eye tracking data with additional events. In config/remodnav_config.json are run-specific parameters defined. In particular, we calibrated the REMoDNaV on self-annotated eye tracking data to the current parameter settings.
• eye_feature_engineering: Our custom feature engineering pipeline to create features for the prediction of drunk drivers. Several parameters can be changed in config/feature_engineering_config.json
• prediction: Here, we provide the output of our main analysis for our paper.
• examples: In this folder, we provide a simple dataset that we recorded with the Tobii Nano Pro to test our pipeline. eye_event_classification and eye_feature_engineering can be executed with this sample data.

Citation

Please cite our paper in any published work that uses any of these resources.

BiBTeX:

@inproceedings{10.1145/3544548.3580975,
author = {Koch, Kevin and Maritsch, Martin and van Weenen, Eva and Feuerriegel, Stefan and Pfäffli, Matthias and Fleisch, Elgar and Weinmann, Wolfgang and Wortmann, Felix},
title = {Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving},
year = {2023},
isbn = {97814503942152304},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544548.3580975},
doi = {10.1145/3544548.3580975},
booktitle = {Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems},
articleno = {293},
numpages = {32},
keywords = {Field study, Mindfulness, In-vehicle interventions, Music, Natural driving, Psychology, Well-being},
location = {Hamburg, Germany},
series = {CHI '23}
}

ACM Ref Citation:

Kevin Koch, Martin Maritsch, Eva van Weenen, Stefan Feuerriegel, Matthias Pfäffli, Elgar Fleisch, Wolfgang Weinmann, and Felix Wortmann. 2023. Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 32 pages. https://doi.org/10.1145/3544548.3580975

Contact

Please contact Kevin Koch or Martin Maritsch for questions.