Drunk DrivingDRIVENCT05796609
Design and Evaluation of an In-Vehicle Real-Time Drunk Driving Detection System
A study testing Driving under the influence of alcohol in people with Drunk Driving and Alcohol Drinking, run by University of Bern at 1 site in Switzerland.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 21 years and olderHealthy volunteers can take part
- Conditions the study is for
- Drunk Driving, Alcohol Drinking, Impaired Driving
- What is being tested?
- Driving under the influence of alcohol
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 55 participants
- Where
- 1 siteSwitzerland
- Study dates
- 5 Apr 2023to 1 Aug 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Informed consent as documented by signature.
- In possession of a definite Swiss or EU driving license.
- At least 21 years old
- Active driving in the last 6 months.
- No special equipment needed when driving.
- Drinks alcohol at least occasionally (moderate/social consumption).
- Fluent in (Swiss) German and no speech impairment.
Who cannot join
- Health concerns that are incompatible with alcohol consumption.
- Any potential participant currently taking illegal drugs or medications that interact with alcohol.
- Women who are pregnant or breast feeding.
- Intention to become pregnant during the course of the study.
- Teetotallers (alcohol abstinent persons).
- Alcohol misuse (excessive alcohol consumption habits/risky drinking behaviour (according to WHO definition) and/or the biomarker PEth in capillary blood > 200 ng/mL at first visit.
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- Known or suspected drug abuse within 4 weeks before the study (e.g., positive urine drug test at first visit).
- Non-compliance to alcohol abstinence within 24 hours before the study visits.
- Inability to follow the procedures of the study, e.g., due to language problems, psychological disorders, dementia, etc. of the participant.
- Participation in another study with investigational drug within the 30 days preceding and during the present study.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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
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.
Read the detailed description
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).
- Conditions
- Drunk Driving, Alcohol Drinking, Impaired Driving
- Being tested
- Driving under the influence of alcohol, Driving under the influence of a placebo
- Keywords
- Driving, Automotive Technology, Car, Alcohol Biomarker, Test Track
- Medical subject headings
- Driving Under the Influence, Alcohol Drinking, Carney Complex
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Driving under the influence of alcoholOther
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
Driving under the influence of a placeboOther
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
| Study group | Role | What they receive |
|---|---|---|
| Treatment Group | Experimental | Driving under the influence of alcohol
Aware of the possible induction of alcohol (purpose of the study), but blinded to the actual amount and target blood alcohol concentration Receives: Other: Driving under the influence of alcohol |
| Reference Group | No intervention | Driving without the influence of alcohol or placebo Fully informed |
| Placebo Group | Placebo comparator | Driving under the influence of a placebo
Not informed (blinded) Receives: Other: Driving under the influence of a placebo |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy of the drunk driving warning system (DRIVE) to detect states of alcohol influence while driving quantified as the Area Under the Receiver Operator Characteristics Curve (AUROC)
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.
Measured: 480 minutes
Secondary outcomes (30)
Diagnostic accuracy of the drunk driving warning system using physiological data to detect states of alcohol influence quantified as the Area Under the Receiver Operator Characteristics Curve (AUROC)
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.
Measured: 480 minutes
Diagnostic accuracy of the drunk driving warning system using eye-tracking data to detect states of alcohol influence quantified as the AUROC
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.
Measured: 480 minutes
Diagnostic accuracy of the drunk driving warning system using controller area network data of the study car to detect states of alcohol influence quantified as the AUROC
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.
Measured: 480 minutes
Diagnostic accuracy of the drunk driving warning system using audio data to detect states of alcohol influence quantified as the AUROC
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.
Measured: 480 minutes
Diagnostic accuracy of the drunk driving warning system using radar sensor data to detect states of alcohol influence quantified as the AUROC
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.
Measured: 480 minutes
Diagnostic accuracy of the drunk driving warning system using gas sensor data to detect states of alcohol influence quantified as the AUROC
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.
Measured: 480 minutes
Change of steering over the alcohol intoxication trajectory
Steering is recorded based on the controller area network.
Measured: 480 minutes
Change of steer torque over the alcohol intoxication trajectory
Steer torque is recorded based on the controller area network.
Measured: 480 minutes
Change of steer speed over the alcohol intoxication trajectory
Steer speed is recorded based on the controller area network.
Measured: 480 minutes
Change of velocity over the alcohol intoxication trajectory
Velocity is recorded based on the controller area network.
Measured: 480 minutes
Change of acceleration over the alcohol intoxication trajectory
Acceleration is recorded based on the controller area network.
Measured: 480 minutes
Change of braking over the alcohol intoxication trajectory
Braking is recorded based on the controller area network.
Measured: 480 minutes
Change of swerving over the alcohol intoxication trajectory
Swerving is recorded based on the controller area network.
Measured: 480 minutes
Change of spinning over the alcohol intoxication trajectory
Spinning is recorded based on the controller area network.
Measured: 480 minutes
Change of gaze position over the alcohol intoxication trajectory
Gaze position is recorded using an eye-tracker device.
Measured: 480 minutes
Change of gaze velocity over the alcohol intoxication trajectory
Gaze velocity is recorded using an eye-tracker device.
Measured: 480 minutes
Change of gaze acceleration over the alcohol intoxication trajectory
Gaze acceleration is recorded using an eye-tracker device.
Measured: 480 minutes
Change of gaze regions of interest over the alcohol intoxication trajectory
Gaze regions of interest (e.g., windshield, car dashboard, etc.) are recorded using an eye-tracker device.
Measured: 480 minutes
Change of gaze events over the alcohol intoxication trajectory
Gaze events (e.g., fixations, saccades, etc.) are recorded using an eye-tracker device.
Measured: 480 minutes
Change of head pose over the alcohol intoxication trajectory
Head pose (position/rotation) is recorded using an eye-tracker device.
Measured: 480 minutes
Change of heart rate over the alcohol intoxication trajectory
Heart rate is recorded using a heart rate monitoring device and wearables.
Measured: 480 minutes
Change of heart rate variability over the alcohol intoxication trajectory
Heart rate variability is recorded using a heart rate monitoring device and wearables.
Measured: 480 minutes
Change of electrodermal activity over the alcohol intoxication trajectory
Electrodermal activity is recorded using wearables.
Measured: 480 minutes
Change of wrist accelerometer measurements over the alcohol intoxication trajectory
Wrist accelerometer measurements are recorded using wearables.
Measured: 480 minutes
Change of skin temperature over the alcohol intoxication trajectory
Skin temperature is recorded using wearables.
Measured: 480 minutes
Self-assessment of driving performance over the alcohol intoxication trajectory
Participants rate their driving performance on a 7-point Likert Scale (lower value means poorer driving performance).
Measured: 480 minutes
Self-estimation of alcohol concentrations over the alcohol intoxication trajectory
Participants estimate their blood alcohol concentration.
Measured: 480 minutes
Number of driving mishaps over the alcohol intoxication trajectory
Any driving mishaps, accidents and interventions by the driving instructor will be documented.
Measured: 480 minutes
Number of Adverse Events (AEs)
Adverse Events will be recorded at each study visit.
Measured: 3 months, from screening to close out visit for each participant
Number of Serious Adverse Events (SAEs)
Serious Adverse Events will be recorded at each study visit.
Measured: 3 months, from screening to close out visit for each participant.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Apr 2023
- Study start5 Apr 2023
- Primary completion1 Aug 2023
- Study completion1 Aug 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Drunk DrivingStudies of the same condition, open and under way.
- More trials run by University of BernThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Treatment group (32 participants); Reference group (12 participants); Placebo group (12 participants)
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 55 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Bern
- Sponsor type
- University, hospital or other
- Collaborators
- ETH Zurich, University of St.Gallen
- Responsible party
- Sponsor
- Investigators
- Wolfgang Weinmann, Prof. Dr., Principal investigator, University of Bern
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05796609
- Sponsor's protocol ID
- DRIVE Test Track
- Other IDs
- SNCTP000005396
- Acronym
- DRIVE
- Status last verified
- Nov 2023
- First submitted
- 8 Mar 2023
- Last updated
- 3 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT05796609
- Acronym / sponsor ID
- DRIVE / DRIVE Test Track
- Conditions
- Drunk Driving, Alcohol Drinking, Impaired Driving
- Keywords
- Driving, Automotive Technology, Car, Alcohol Biomarker, Test Track
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 55 participants (actual)
- Lead sponsor
- University of BernUniversity, hospital or other
- Collaborators
- ETH Zurich, University of St.Gallen
- Study start
- 5 Apr 2023 (actual)
- Primary completion
- 1 Aug 2023 (actual)
- Study completion
- 1 Aug 2023 (actual)
- First posted
- 3 Apr 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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).
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 3
- Model description
- Treatment group (32 participants); Reference group (12 participants); Placebo group (12 participants)
| Arm | Type | Receives |
|---|---|---|
| Treatment Group | Experimental | Other: Driving under the influence of alcoholDriving under the influence of alcohol Aware of the possible induction of alcohol (purpose of the study), but blinded to the actual amount and target blood alcohol concentration |
| Reference Group | No intervention | –Driving without the influence of alcohol or placebo Fully informed |
| Placebo Group | Placebo comparator | Other: Driving under the influence of a placeboDriving under the influence of a placebo Not informed (blinded) |
Who may qualify
- Ages
- 21 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (7 listed)
- Informed consent as documented by signature.
- In possession of a definite Swiss or EU driving license.
- At least 21 years old
- Active driving in the last 6 months.
- No special equipment needed when driving.
Key exclusion criteria (10 listed)
- Health concerns that are incompatible with alcohol consumption.
- Any potential participant currently taking illegal drugs or medications that interact with alcohol.
- Women who are pregnant or breast feeding.
- Intention to become pregnant during the course of the study.
- Teetotallers (alcohol abstinent persons).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Driving under the influence of alcoholParticipants 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 | Other | Treatment Group |
| Driving under the influence of a placeboParticipants 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 | Other | Placebo Group |
Primary outcome
Diagnostic accuracy of the drunk driving warning system (DRIVE) to detect states of alcohol influence while driving quantified as the Area Under the Receiver Operator Characteristics Curve (AUROC)
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.
30 secondary outcomes
Diagnostic accuracy of the drunk driving warning system using physiological data to detect states of alcohol influence quantified as the Area Under the Receiver Operator Characteristics Curve (AUROC)
Time frame: 480 minutes
Diagnostic accuracy of the drunk driving warning system using eye-tracking data to detect states of alcohol influence quantified as the AUROC
Time frame: 480 minutes
Diagnostic accuracy of the drunk driving warning system using controller area network data of the study car to detect states of alcohol influence quantified as the AUROC
Time frame: 480 minutes
Diagnostic accuracy of the drunk driving warning system using audio data to detect states of alcohol influence quantified as the AUROC
Time frame: 480 minutes
Diagnostic accuracy of the drunk driving warning system using radar sensor data to detect states of alcohol influence quantified as the AUROC
Time frame: 480 minutes
Diagnostic accuracy of the drunk driving warning system using gas sensor data to detect states of alcohol influence quantified as the AUROC
Time frame: 480 minutes
Change of steering over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of steer torque over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of steer speed over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of velocity over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of acceleration over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of braking over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of swerving over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of spinning over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of gaze position over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of gaze velocity over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of gaze acceleration over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of gaze regions of interest over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of gaze events over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of head pose over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of heart rate over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of heart rate variability over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of electrodermal activity over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of wrist accelerometer measurements over the alcohol intoxication trajectory
Time frame: 480 minutes
Change of skin temperature over the alcohol intoxication trajectory
Time frame: 480 minutes
Self-assessment of driving performance over the alcohol intoxication trajectory
Time frame: 480 minutes
Self-estimation of alcohol concentrations over the alcohol intoxication trajectory
Time frame: 480 minutes
Number of driving mishaps over the alcohol intoxication trajectory
Time frame: 480 minutes
Number of Adverse Events (AEs)
Time frame: 3 months, from screening to close out visit for each participant
Number of Serious Adverse Events (SAEs)
Time frame: 3 months, from screening to close out visit for each participant.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Apr 2026.
- Individual participant dataSharing planned: No
Related trials for Drunk Driving
Recruiting
Evaluation of Global Smart Drinking Goals Initiative
Binge Drinking, Underage Drinking, Drinking and Driving, Alcohol-Related Harms
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Wolfgang Weinmann, Prof. Dr. principal investigator
University of Bern
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Driving under the influence of alcohol in people with Drunk Driving and Alcohol Drinking, run by University of Bern at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 10 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 3 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of the drunk driving warning system (DRIVE) to detect states of alcohol influence while driving quantified as the Area Under the Receiver Operator Characteristics Curve (AUROC) (time frame: 480 minutes). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Apr 2026.