Academic Journal
How machine learning has been used to detect alcohol-induced driver impairment using in-vehicle sensors: A systematic review.
| Τίτλος: | How machine learning has been used to detect alcohol-induced driver impairment using in-vehicle sensors: A systematic review. |
|---|---|
| Συγγραφείς: | Devcich BG; MAIC/UniSC Road Safety Research Collaboration, University of the Sunshine Coast, Sippy Downs, Queensland, 4556, Australia., Ang LM; School of Science, Technology and Engineering, University of the Sunshine Coast, Petrie, Queensland, 4502, Australia., Wang M; School of Science, Technology and Engineering, University of the Sunshine Coast, Petrie, Queensland, 4502, Australia., Larue GS; MAIC/UniSC Road Safety Research Collaboration, University of the Sunshine Coast, Sippy Downs, Queensland, 4556, Australia. Electronic address: glarue@usc.edu.au. |
| Πηγή: | Journal of safety research [J Safety Res] 2026 Jun; Vol. 97, pp. 52-66. Date of Electronic Publication: 2026 Feb 14. |
| Τύπος έκδοσης: | Journal Article; Systematic Review |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: United States NLM ID: 1264241 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1247 (Electronic) Linking ISSN: 00224375 NLM ISO Abbreviation: J Safety Res Subsets: MEDLINE |
| Imprint Name(s): | Publication: Elmsford, NY : Pergamon Press Original Publication: [Chicago] National Safety Council. |
| Ιατρικοί όροι (MeSH): | Machine Learning* , Driving Under the Influence* , Automobile Driving*, Accidents, Traffic/prevention & control ; Humans ; Reproducibility of Results ; Predictive Learning Models |
| Περίληψη: | 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. (Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Advanced driver assistance systems; Blood alcohol concentration; Classification; Intoxication; Neural network; Road safety |
| Entry Date(s): | Date Created: 20260615 Date Completed: 20260615 Latest Revision: 20260615 |
| Update Code: | 20260616 |
| DOI: | 10.1016/j.jsr.2026.01.021 |
| PMID: | 42297503 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1879-1247 |
|---|---|
| DOI: | 10.1016/j.jsr.2026.01.021 |