Clinical trial NCT05796609: Design and Evaluation of an In-Vehicle Real-Time Drunk Driving Detection System
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
Overall status: COMPLETED
Phase: NA
Conditions: Drunk Driving
Alcohol Drinking
Impaired Driving
Keywords: Driving
Automotive Technology
Car
Alcohol Biomarker
Test Track
Lead sponsor: University of Bern
Sponsor class: OTHER
Collaborators: name: ETH Zurich
name: University of St.Gallen
First posted: 2023-04-03
Enrollment: 55
Sex: ALL
Minimum age (years): 21
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Treatment group (32 participants); Reference group (12 participants); Placebo group (12 participants)
Primary purpose: OTHER

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).

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.

Primary outcomes:
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)
timeFrame: 480 minutes
Secondary outcomes:
measure: 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)
timeFrame: 480 minutes
measure: Diagnostic accuracy of the drunk driving warning system using eye-tracking data to detect states of alcohol influence quantified as the AUROC
timeFrame: 480 minutes
measure: 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
timeFrame: 480 minutes
measure: Diagnostic accuracy of the drunk driving warning system using audio data to detect states of alcohol influence quantified as the AUROC
timeFrame: 480 minutes
measure: Diagnostic accuracy of the drunk driving warning system using radar sensor data to detect states of alcohol influence quantified as the AUROC
timeFrame: 480 minutes
measure: Diagnostic accuracy of the drunk driving warning system using gas sensor data to detect states of alcohol influence quantified as the AUROC
timeFrame: 480 minutes
measure: Change of steering over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of steer torque over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of steer speed over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of velocity over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of acceleration over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of braking over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of swerving over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of spinning over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of gaze position over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of gaze velocity over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of gaze acceleration over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of gaze regions of interest over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of gaze events over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of head pose over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of heart rate over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of heart rate variability over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of electrodermal activity over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of wrist accelerometer measurements over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Change of skin temperature over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Self-assessment of driving performance over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Self-estimation of alcohol concentrations over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Number of driving mishaps over the alcohol intoxication trajectory
timeFrame: 480 minutes
measure: Number of Adverse Events (AEs)
timeFrame: 3 months, from screening to close out visit for each participant
measure: Number of Serious Adverse Events (SAEs)
timeFrame: 3 months, from screening to close out visit for each participant.
Other outcomes:
Locations:
city: Bern
country: Switzerland
facility: Institut für Rechtsmedizin
Study officials:
affiliation: University of Bern
name: Wolfgang Weinmann, Prof. Dr.
role: PRINCIPAL_INVESTIGATOR
References: