Journal article
How machine learning has been used to detect alcohol-induced driver impairment using in-vehicle sensors: A systematic review
Journal of Safety Research, Vol.97, pp.52-66
2026
Abstract
Introduction: Alcohol-impaired driving is a persistent global public health concern, with limited recent progress in reducing its impact on roads, highlighting the need for innovative approaches. The increasing adoption of machine learning (ML) has led to its application in detecting alcohol-induced driving impairment. This systematic review examines and synthesizes existing research that leverages ML utilizing in-vehicle sensors to detect alcohol-impaired driving, with a focus on the ML models employed and the input variables analyzed. Method: Studies were included if they applied ML techniques to detect alcohol-induced driving impairment using in-vehicle sensor data. The literature search was conducted in various academic databases and was supplemented by citation-based article retrieval. Primary outcomes of interest were the ML models employed and input variables. A risk of bias assessment was performed to evaluate study reliability and validity. Results: There were 26 relevant studies identified. The reported classification accuracy was consistently high, with median accuracy of 89%. Although the majority of studies were performed using driving simulators, there was significant heterogeneity with respect to other important study characteristics. The most common input variables used related to vehicle dynamics and control inputs, and the most common ML models implemented were neural networks. Conclusions and Practical Applications: This systematic review highlights limitations in the current literature related to significant heterogeneity in study characteristics and methodological issues in many identified studies. While some promising results were observed, further research is required to determine the optimal approach, particularly with respect to finding the most compatible and practical ML models and input variables for reliable detection.
Details
- Title
- How machine learning has been used to detect alcohol-induced driver impairment using in-vehicle sensors: A systematic review
- Authors
- Brent G. Devcich - University of the Sunshine Coast, Queensland, Road Safety Research CollaborationLi-minn Ang - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringMingzhong Wang - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringGrégoire S. Larue (Corresponding Author) - University of the Sunshine Coast, Queensland, Road Safety Research Collaboration
- Publication details
- Journal of Safety Research, Vol.97, pp.52-66
- Publisher
- Elsevier Ltd
- Date published
- 2026
- DOI
- 10.1016/j.jsr.2026.01.021
- ISSN
- 1879-1247
- Copyright note
- © 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
- Grant note
- The Motor Accident Insurance Commission provided funding to the University of the Sunshine Coast to conduct research activities that aim to reduce the incidence of motor vehicle crashes.
- Organisation Unit
- Centre for Human Factors and Systems Science; Road Safety Research Collaboration; Healthy Ageing Research Cluster; School of Science, Technology and Engineering; Engage Research Lab; School of Law and Society
- Language
- English
- Record Identifier
- 991210047602621
- Output Type
- Journal article
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InCites Highlights
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- Web Of Science research areas
- Ergonomics
- Public, Environmental & Occupational Health
- Social Sciences, Interdisciplinary
- Transportation