# Detecting Drunk Driving Using Off-the-Shelf Smartwatches

arXiv: https://arxiv.org/abs/2605.23663 · HTML: https://mlsanity.com/paper/2605.23663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 69 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 2 of 2)

- [Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving](https://mlsanity.com/paper/2301.08978/) (2023)
- [Advancing Intoxication Detection: A Smartwatch-Based Approach](https://mlsanity.com/paper/2510.09916/) (2025)

## Abstract (arXiv)

Alcohol-impaired driving remains a major yet preventable cause of road traffic injury and death, with many drivers underestimating their level of intoxication. Compared to in-vehicle systems, mobile drunk-driving detection using consumer smartwatches offers a scalable way to trigger preventive interventions and increase awareness without additional in-vehicle hardware. We introduce a system that leverages wrist accelerometer data and heart rate variability-derived physiological signals to detect alcohol-related driving impairment. We collected data in a randomized, controlled three-arm test-track study (n=54) and trained both logistic regression models with window-aggregated features and a two-tower 1D convolutional neural network (CNN), to detect alcohol-impaired driving. The CNN achieved a participant-averaged area under the receiver operating characteristic (AUROC) of 0.88 for detecting any alcohol intoxication and 0.86 for detecting driving above the WHO-recommended limit of 0.05 g/dL. To the best of our knowledge, this is the first work to (1) demonstrate drunk-driving detection using consumer smartwatches, (2) develop and evaluate such a system in a real vehicle on a closed test track, and (3) rigorously assess generalization to unseen participants. Together, these findings highlight the potential of wearable-based sensing to support scalable, measurement-driven prevention of alcohol-related traffic harm.