Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving-Reference-Cited by-同舟云学术

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

Published:2023-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
language:
Short-container-title:

Author:

Koch Kevin¹^ORCID,Maritsch Martin²^ORCID,Van Weenen Eva²^ORCID,Feuerriegel Stefan³^ORCID,Pfäffli Matthias⁴^ORCID,Fleisch Elgar⁵^ORCID,Weinmann Wolfgang⁴^ORCID,Wortmann Felix¹^ORCID

Affiliation:

1. Institute of Technoly Management, University of St. Gallen, Switzerland

2. Department of Management, Technology, and Economics, ETH Zurich, Switzerland

3. School of Management, LMU Munich, Germany

4. Institute of Forensic Medicine, University of Bern, Switzerland

5. Department of Management, Technology, and Economics, ETH Zurich, Switzerland and Institute of Technoly Management, University of St. Gallen, Switzerland

Funder

Swiss National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3544548.3580975

Reference122 articles.

1. YawDD

2. Affectiva. 2022. Affectiva Automotive AI. http://go.affectiva.com/auto Accessed: 08/11/2022. Affectiva. 2022. Affectiva Automotive AI. http://go.affectiva.com/auto Accessed: 08/11/2022.

3. Kirstin Aschbacher , Christian S. Hendershot , Geoffrey Tison , Judith A. Hahn , Robert Avram , Jeffrey E. Olgin , and Gregory M. Marcus . 2021. Machine learning prediction of blood alcohol concentration: a digital signature of smart-breathalyzer behavior. npj Digital Medicine 4, 1 ( 2021 ), 74. https://doi.org/10.1038/s41746-021-00441-4 10.1038/s41746-021-00441-4 Kirstin Aschbacher, Christian S. Hendershot, Geoffrey Tison, Judith A. Hahn, Robert Avram, Jeffrey E. Olgin, and Gregory M. Marcus. 2021. Machine learning prediction of blood alcohol concentration: a digital signature of smart-breathalyzer behavior. npj Digital Medicine 4, 1 (2021), 74. https://doi.org/10.1038/s41746-021-00441-4

4. Self-estimation of blood alcohol concentration: A review

5. Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions

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