Back to resultsDesign and Evaluation of an In-Vehicle Real-Time Drunk Driving Detection System (DRIVE)
Primary Purpose
Drunk Driving, Alcohol Drinking, Impaired Driving
Status
Recruiting
Phase
Not Applicable
Locations
Switzerland
Study Type
Interventional
Intervention
Driving under the influence of alcohol
Driving under the influence of a placebo
Sponsored by
About this trial
This is an interventional other trial for Drunk Driving focused on measuring Driving, Automotive Technology, Car, Alcohol Biomarker, Test Track
Eligibility Criteria
Inclusion Criteria: 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. Exclusion Criteria: 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. 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.
Sites / Locations
- Institut für RechtsmedizinRecruiting
Arms of the Study
Arm 1
Arm 2
Arm 3
Arm Type
Experimental
No Intervention
Placebo Comparator
Arm Label
Treatment Group
Reference Group
Placebo Group
Arm Description
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
Driving without the influence of alcohol or placebo Fully informed
Driving under the influence of a placebo Not informed (blinded)
Outcomes
Primary Outcome Measures
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.
Secondary Outcome Measures
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.
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.
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.
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.
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.
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.
Change of steering over the alcohol intoxication trajectory
Steering is recorded based on the controller area network.
Change of steer torque over the alcohol intoxication trajectory
Steer torque is recorded based on the controller area network.
Change of steer speed over the alcohol intoxication trajectory
Steer speed is recorded based on the controller area network.
Change of velocity over the alcohol intoxication trajectory
Velocity is recorded based on the controller area network.
Change of acceleration over the alcohol intoxication trajectory
Acceleration is recorded based on the controller area network.
Change of braking over the alcohol intoxication trajectory
Braking is recorded based on the controller area network.
Change of swerving over the alcohol intoxication trajectory
Swerving is recorded based on the controller area network.
Change of spinning over the alcohol intoxication trajectory
Spinning is recorded based on the controller area network.
Change of gaze position over the alcohol intoxication trajectory
Gaze position is recorded using an eye-tracker device.
Change of gaze velocity over the alcohol intoxication trajectory
Gaze velocity is recorded using an eye-tracker device.
Change of gaze acceleration over the alcohol intoxication trajectory
Gaze acceleration is recorded using an eye-tracker device.
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.
Change of gaze events over the alcohol intoxication trajectory
Gaze events (e.g., fixations, saccades, etc.) are recorded using an eye-tracker device.
Change of head pose over the alcohol intoxication trajectory
Head pose (position/rotation) is recorded using an eye-tracker device.
Change of heart rate over the alcohol intoxication trajectory
Heart rate is recorded using a heart rate monitoring device and wearables.
Change of heart rate variability over the alcohol intoxication trajectory
Heart rate variability is recorded using a heart rate monitoring device and wearables.
Change of electrodermal activity over the alcohol intoxication trajectory
Electrodermal activity is recorded using wearables.
Change of wrist accelerometer measurements over the alcohol intoxication trajectory
Wrist accelerometer measurements are recorded using wearables.
Change of skin temperature over the alcohol intoxication trajectory
Skin temperature is recorded using wearables.
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).
Self-estimation of alcohol concentrations over the alcohol intoxication trajectory
Participants estimate their blood alcohol concentration.
Number of driving mishaps over the alcohol intoxication trajectory
Any driving mishaps, accidents and interventions by the driving instructor will be documented.
Number of Adverse Events (AEs)
Adverse Events will be recorded at each study visit.
Number of Serious Adverse Events (SAEs)
Serious Adverse Events will be recorded at each study visit.
Full Information
NCT ID
NCT05796609
First Posted
March 8, 2023
Last Updated
May 9, 2023
Sponsor
University of Bern
Collaborators
ETH Zurich, University of St.Gallen
1. Study Identification
Unique Protocol Identification Number
NCT05796609
Brief Title
Design and Evaluation of an In-Vehicle Real-Time Drunk Driving Detection System
Acronym
DRIVE
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
Study Type
Interventional
2. Study Status
Record Verification Date
May 2023
Overall Recruitment Status
Recruiting
Study Start Date
April 5, 2023 (Actual)
Primary Completion Date
September 30, 2023 (Anticipated)
Study Completion Date
September 30, 2023 (Anticipated)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Sponsor
Name of the Sponsor
University of Bern
Collaborators
ETH Zurich, University of St.Gallen
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No
5. Study Description
Brief 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.
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).
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Drunk Driving, Alcohol Drinking, Impaired Driving
Keywords
Driving, Automotive Technology, Car, Alcohol Biomarker, Test Track
7. Study Design
Primary Purpose
Other
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Model Description
Treatment group (32 participants); Reference group (12 participants); Placebo group (12 participants)
Masking
Participant
Masking Description
The participants of the treatment group are aware of the possible induction of alcohol (purpose of the study), but blinded to the actual amount and target blood alcohol concentration. The participants of the reference group are fully informed that they do not get alcohol (open-label). The participants of the placebo group are not informed that they do not get alcohol but a placebo (blinded).
Allocation
Randomized
Enrollment
56 (Anticipated)
8. Arms, Groups, and Interventions
Arm Title
Treatment Group
Arm Type
Experimental
Arm Description
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
Arm Title
Reference Group
Arm Type
No Intervention
Arm Description
Driving without the influence of alcohol or placebo
Fully informed
Arm Title
Placebo Group
Arm Type
Placebo Comparator
Arm Description
Driving under the influence of a placebo
Not informed (blinded)
Intervention Type
Other
Intervention Name(s)
Driving under the influence of alcohol
Intervention Description
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
Intervention Type
Other
Intervention Name(s)
Driving under the influence of a placebo
Intervention Description
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
Primary Outcome Measure Information:
Title
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)
Description
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
Secondary Outcome Measure Information:
Title
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)
Description
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
Title
Diagnostic accuracy of the drunk driving warning system using eye-tracking data to detect states of alcohol influence quantified as the AUROC
Description
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
Title
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
Description
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
Title
Diagnostic accuracy of the drunk driving warning system using audio data to detect states of alcohol influence quantified as the AUROC
Description
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
Title
Diagnostic accuracy of the drunk driving warning system using radar sensor data to detect states of alcohol influence quantified as the AUROC
Description
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
Title
Diagnostic accuracy of the drunk driving warning system using gas sensor data to detect states of alcohol influence quantified as the AUROC
Description
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
Title
Change of steering over the alcohol intoxication trajectory
Description
Steering is recorded based on the controller area network.
Time Frame
480 minutes
Title
Change of steer torque over the alcohol intoxication trajectory
Description
Steer torque is recorded based on the controller area network.
Time Frame
480 minutes
Title
Change of steer speed over the alcohol intoxication trajectory
Description
Steer speed is recorded based on the controller area network.
Time Frame
480 minutes
Title
Change of velocity over the alcohol intoxication trajectory
Description
Velocity is recorded based on the controller area network.
Time Frame
480 minutes
Title
Change of acceleration over the alcohol intoxication trajectory
Description
Acceleration is recorded based on the controller area network.
Time Frame
480 minutes
Title
Change of braking over the alcohol intoxication trajectory
Description
Braking is recorded based on the controller area network.
Time Frame
480 minutes
Title
Change of swerving over the alcohol intoxication trajectory
Description
Swerving is recorded based on the controller area network.
Time Frame
480 minutes
Title
Change of spinning over the alcohol intoxication trajectory
Description
Spinning is recorded based on the controller area network.
Time Frame
480 minutes
Title
Change of gaze position over the alcohol intoxication trajectory
Description
Gaze position is recorded using an eye-tracker device.
Time Frame
480 minutes
Title
Change of gaze velocity over the alcohol intoxication trajectory
Description
Gaze velocity is recorded using an eye-tracker device.
Time Frame
480 minutes
Title
Change of gaze acceleration over the alcohol intoxication trajectory
Description
Gaze acceleration is recorded using an eye-tracker device.
Time Frame
480 minutes
Title
Change of gaze regions of interest over the alcohol intoxication trajectory
Description
Gaze regions of interest (e.g., windshield, car dashboard, etc.) are recorded using an eye-tracker device.
Time Frame
480 minutes
Title
Change of gaze events over the alcohol intoxication trajectory
Description
Gaze events (e.g., fixations, saccades, etc.) are recorded using an eye-tracker device.
Time Frame
480 minutes
Title
Change of head pose over the alcohol intoxication trajectory
Description
Head pose (position/rotation) is recorded using an eye-tracker device.
Time Frame
480 minutes
Title
Change of heart rate over the alcohol intoxication trajectory
Description
Heart rate is recorded using a heart rate monitoring device and wearables.
Time Frame
480 minutes
Title
Change of heart rate variability over the alcohol intoxication trajectory
Description
Heart rate variability is recorded using a heart rate monitoring device and wearables.
Time Frame
480 minutes
Title
Change of electrodermal activity over the alcohol intoxication trajectory
Description
Electrodermal activity is recorded using wearables.
Time Frame
480 minutes
Title
Change of wrist accelerometer measurements over the alcohol intoxication trajectory
Description
Wrist accelerometer measurements are recorded using wearables.
Time Frame
480 minutes
Title
Change of skin temperature over the alcohol intoxication trajectory
Description
Skin temperature is recorded using wearables.
Time Frame
480 minutes
Title
Self-assessment of driving performance over the alcohol intoxication trajectory
Description
Participants rate their driving performance on a 7-point Likert Scale (lower value means poorer driving performance).
Time Frame
480 minutes
Title
Self-estimation of alcohol concentrations over the alcohol intoxication trajectory
Description
Participants estimate their blood alcohol concentration.
Time Frame
480 minutes
Title
Number of driving mishaps over the alcohol intoxication trajectory
Description
Any driving mishaps, accidents and interventions by the driving instructor will be documented.
Time Frame
480 minutes
Title
Number of Adverse Events (AEs)
Description
Adverse Events will be recorded at each study visit.
Time Frame
3 months, from screening to close out visit for each participant
Title
Number of Serious Adverse Events (SAEs)
Description
Serious Adverse Events will be recorded at each study visit.
Time Frame
3 months, from screening to close out visit for each participant.
10. Eligibility
Sex
All
Minimum Age & Unit of Time
21 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
Inclusion Criteria:
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.
Exclusion Criteria:
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.
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.
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Robin Deuber
Phone
+41 44 632 57 28
Email
rdeuber@ethz.ch
First Name & Middle Initial & Last Name or Official Title & Degree
Wolfgang Weinmann, Prof. Dr.
Phone
+41 79 437 25 97
Email
wolfgang.weinmann@irm.unibe.ch
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Wolfgang Weinmann, Prof. Dr.
Organizational Affiliation
University of Bern
Official's Role
Principal Investigator
Facility Information:
Facility Name
Institut für Rechtsmedizin
City
Bern
ZIP/Postal Code
3008
Country
Switzerland
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Wolfgang Weinmann, Prof. Dr.
Phone
+41 79 4372597
Email
wolfgang.weinmann@irm.unibe.ch
12. IPD Sharing Statement
Plan to Share IPD
No
Learn more about this trial
Design and Evaluation of an In-Vehicle Real-Time Drunk Driving Detection System
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