Detecting Drunk Driving Using Off-the-Shelf Smartwatches

• URL: http://arxiv.org/abs/2605.23663v1
• Date: Fri, 22 May 2026 14:13:28 GMT
• Title: Detecting Drunk Driving Using Off-the-Shelf Smartwatches
• Abstract summary: Mobile drunk-driving detection using consumer smartwatches offers a scalable way to trigger preventive interventions.<n>We introduce a system that leverages wrist accelerometer data and heart rate variability-derived physiological signals to detect alcohol-related driving impairment.
• Score: 7.484358451123682
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens [47.03825808787752]
  We present empirical findings from a human-subject study in which users classified news fragments by origin (human vs. machine) and veracity (real vs. fake)<n>We organize results using an adapted cybersecurity kill chain as a taxonomy for intervention, mapping perception data onto stages of a cognitive attack lifecycle.<n>Three key findings emerge: (1) a perception-accuracy gap where heightened suspicion does not improve detection; (2) modern LLMs frequently produce human-indistinguishable text; and (3) an asymmetric cognitive fatigue effect where fake-news detection degrades by 10.2 percentage points under sustained exposure while AI-origin detection remains stable.
  arXiv  Detail & Related papers  (2026-07-26T15:40:41Z)
• Detection of Intoxicated Individuals from Facial Video Sequences via a Recurrent Fusion Model [0.4779196219827507]
  This study introduces a novel-based facial sequence analysis approach dedicated to the detection of alcohol intoxication.<n>The method integrates facial landmark analysis via a Graph Attention Network (GAT) with visual features extracted using 3D ResNet.<n> Experimental results show that our approach achieves 95.82% accuracy, 0.977 precision, and 0.97 recall, outperforming prior methods.
  arXiv  Detail & Related papers  (2025-12-04T07:34:04Z)
• Mobile Phone Sensor-based Nigerian Driving Dataset to Detect Alcohol-influenced Behaviours [0.0]
  This paper presents a unique driving dataset collected in Nigeria via mobile phone sensors to support a machine learning model for detecting alcohol-influenced driving behaviours.<n>The proposed model leverages smartphone sensors such as accelerometers, gyroscopes, and GPS to provide a non-invasive, continuous solution for detecting impaired driving patterns in real time.
  arXiv  Detail & Related papers  (2025-09-03T15:21:03Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Masked EEG Modeling for Driving Intention Prediction [27.606175591082756]
  This paper pioneers a novel research direction in BCI-assisted driving, studying the neural patterns related to driving intentions. We propose a novel Masked EEG Modeling framework for predicting human driving intentions, including the intention for left turning, right turning, and straight proceeding. Our model attains an accuracy of 85.19% when predicting driving intentions for drowsy subjects, which shows its promising potential for mitigating traffic accidents related to drowsy driving.
  arXiv  Detail & Related papers  (2024-08-08T03:49:05Z)
• RACER: Rational Artificial Intelligence Car-following-model Enhanced by Reality [46.909086734963665]
  This paper introduces RACER, a cutting-edge deep learning car-following model to predict Adaptive Cruise Control (ACC) driving behavior.<n>Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving.<n> RACER excels across key metrics, such as acceleration, velocity, and spacing, registering zero violations.
  arXiv  Detail & Related papers  (2023-12-12T06:21:30Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Vision-based Analysis of Driver Activity and Driving Performance Under the Influence of Alcohol [1.2094859111770522]
  30% of all traffic crash fatalities in the U.S. involve drunk drivers. Driving impairment can be monitored through active use of sensors. More passive and robust mechanism of sensing may allow for wider adoption.
  arXiv  Detail & Related papers  (2023-09-14T20:34:30Z)
• Driving Anomaly Detection Using Conditional Generative Adversarial Network [26.45460503638333]
  This study proposes an unsupervised method to quantify driving anomalies using a conditional generative adversarial network (GAN) The approach predicts upcoming driving scenarios by conditioning the models on the previously observed signals. The results are validated with perceptual evaluations, where annotators are asked to assess the risk and familiarity of the videos detected with high anomaly scores.
  arXiv  Detail & Related papers  (2022-03-15T22:10:01Z)
• Driver2vec: Driver Identification from Automotive Data [44.84876493736275]
  Driver2vec is able to accurately identify the driver from a short 10-second interval of sensor data. Driver2vec is trained on a dataset of 51 drivers provided by Nervtech.
  arXiv  Detail & Related papers  (2021-02-10T03:09:13Z)
• Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring [52.557003792696484]
  We present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. Our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.
  arXiv  Detail & Related papers  (2020-06-20T11:56:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.