Institut für Rechtsmedizin
Bern, 3008, Switzerland
NCT Number: NCT05796609
To analyze driving behavior of individuals under the influence of alcohol while driving in a real car. Based on the in-vehicle variables, the investigators aim at establishing algorithms capable of discriminating sober and drunk driving using machine learning.
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Notify Me21 year and older
All sexes
Interventional
Not applicable
Bern, 3008, Switzerland
Driving under the influence of alcohol (or "drunk driving") is one of the most significant causes of traffic accidents. Alcohol consumption impairs neurocognitive and psychomotor function and has been shown to be associated with an increased risk of driving accidents. However, autonomous driving (level 4 or 5) is likely to be broadly available only at a substantially later time point than previously thought due to increasing concerns of safety associated with this technology. Therefore, solutions bridging the upcoming time period by more rapidly and directly addressing the problem of drunk driving associated traffic incidents are urgently needed.
On the supposition that driving behavior differs significantly between sober state and drunk state, the investigators assume that different driving patterns of people under alcohol influence compared to sober states can be used to generate drunk driving detection models using machine learning algorithms. In this study, driving for data collection is initially performed at a sober baseline state (no alcohol) and then after alcohol administration (with a target of 0.15 mg/l and 0.35 mg/l breath alcohol concentration).
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Participants will drive in three different states (sober, drunk above and below the legal limit) on a designated circuit with a real car on a test track accompanied by a driving instructor. After the initial sober driving session, participants are administered pre-mixed alcoholic beverages (e.g., vodka orange). Participants are expected to achieve a target breath alcohol concentration of 0.35 mg/l (legal limit in Switzerland is 0.25 mg/l breath alcohol concentration) before the second driving session starts. Finally, the third driving session starts when the participants' breath alcohol concentration drops to 0.15 mg/l.
Participants will be blinded to their alcohol levels during the study.
Measurements: Heart rate, respiration rate, blood oxygen saturation, skin conductance, skin temperature, accelerometer, eye movement, radar, facial expression, audio recording, vehicle data, in-cabin gas concentration
Participants will drive three times at the same intervals as the treatment group on a designated circuit with a real car on a test track accompanied by a driving instructor. After the initial driving session, participants receive placebo beverages (e.g., orange juice with vodka flavor).
Participants are fully blinded.
Measurements: Heart rate, respiration rate, blood oxygen saturation, skin conductance, skin temperature, accelerometer, eye movement, radar, facial expression, audio recording, vehicle data, in-cabin gas concentration
Time frame: 480 minutes
The machine learning model is developed and evaluated based on in-vehicle data generated in different states of alcohol intoxication. Detection performance of alcohol influence is quantified as AUROC.
Time frame: 480 minutes
The machine learning model is developed and evaluated based on physiological wearable data recorded in different states of alcohol intoxication. Detection performance of alcohol influence is quantified as AUROC.
Time frame: 480 minutes
The machine learning model is developed and evaluated based on eye-tracking data recorded in different states of alcohol intoxication. Detection performance of alcohol influence is quantified as AUROC.
Time frame: 480 minutes
The machine learning model is developed and evaluated based on controller area network data of the study car recorded in different states of alcohol intoxication. Detection performance of alcohol influence is quantified as AUROC.
Time frame: 480 minutes
The machine learning model is developed and evaluated based on audio data recorded in different states of alcohol intoxication. Detection performance of alcohol influence is quantified as AUROC.
Time frame: 480 minutes
The machine learning model is developed and evaluated based on radar sensor data recorded in different states of alcohol intoxication. Detection performance of alcohol influence is quantified as AUROC.
Time frame: 480 minutes
The machine learning model is developed and evaluated based on gas sensor data recorded in different states of alcohol intoxication. Detection performance of alcohol influence is quantified as AUROC.
Time frame: 480 minutes
Steering is recorded based on the controller area network.
Time frame: 480 minutes
Steer torque is recorded based on the controller area network.
Time frame: 480 minutes
Steer speed is recorded based on the controller area network.
Time frame: 480 minutes
Velocity is recorded based on the controller area network.
Time frame: 480 minutes
Acceleration is recorded based on the controller area network.
Time frame: 480 minutes
Braking is recorded based on the controller area network.
Time frame: 480 minutes
Swerving is recorded based on the controller area network.
Time frame: 480 minutes
Spinning is recorded based on the controller area network.
Time frame: 480 minutes
Gaze position is recorded using an eye-tracker device.
Time frame: 480 minutes
Gaze velocity is recorded using an eye-tracker device.
Time frame: 480 minutes
Gaze acceleration is recorded using an eye-tracker device.
Time frame: 480 minutes
Gaze regions of interest (e.g., windshield, car dashboard, etc.) are recorded using an eye-tracker device.
Time frame: 480 minutes
Gaze events (e.g., fixations, saccades, etc.) are recorded using an eye-tracker device.
Time frame: 480 minutes
Head pose (position/rotation) is recorded using an eye-tracker device.
Time frame: 480 minutes
Heart rate is recorded using a heart rate monitoring device and wearables.
Time frame: 480 minutes
Heart rate variability is recorded using a heart rate monitoring device and wearables.
Time frame: 480 minutes
Electrodermal activity is recorded using wearables.
Time frame: 480 minutes
Wrist accelerometer measurements are recorded using wearables.
Time frame: 480 minutes
Skin temperature is recorded using wearables.
Time frame: 480 minutes
Participants rate their driving performance on a 7-point Likert Scale (lower value means poorer driving performance).
Time frame: 480 minutes
Participants estimate their blood alcohol concentration.
Time frame: 480 minutes
Any driving mishaps, accidents and interventions by the driving instructor will be documented.
Time frame: 3 months, from screening to close out visit for each participant
Adverse Events will be recorded at each study visit.
Time frame: 3 months, from screening to close out visit for each participant.
Serious Adverse Events will be recorded at each study visit.
University of Bern
Other
Randomized, Controlled, Interventional Single-Centre Study for the Design and Evaluation of an In-Vehicle Real-Time Drunk Driving Detection System - The DRIVE Test Track Study
Acronym: DRIVE
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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