Clinical trial NCT04980846: Design and Implementation of a Drunk Driving Detection System
Official title: Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an In-vehicle Drunk Driving Detection System
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Transportation Accidents
Drunk Driving
Alcohol Drinking
Impaired Driving
Keywords: Metabolism
Machine Learning
Automotive Technology
Driving
Driving Simulator
Alcohol biomarker
Cortisol
Phosphatidylethanol
Lead sponsor: University of Bern
Sponsor class: OTHER
Collaborators: name: University of St.Gallen
name: ETH Zurich
First posted: 2021-07-28
Enrollment: 30
Sex: ALL
Minimum age (years): 17
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: OTHER

Brief summary: To analyse driving behavior of individuals under the influence of alcohol using a validated research driving simulator. Based on the driving variables provided by the simulator the investigators aim at establishing algorithms capable of discriminating sober and drunk driving patterns using machine learning neural networks (deep machine learning classifiers).

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. Automotive technology is highly dynamic, and fully autonomous driving might, in the end, resolve the issue of alcohol impaired accidents. However, autonomous driving (level 4 or 5) is likely to be broadly available only to a substantially later time point than previously thought due to increasing concerns of safety associated with this technology. Therefore, solutions bridging the upcoming period by more rapidly and directly addressing the problem of drunk driving-associated traffic incidents are urgently needed.

On the supposition that driving behaviour differs significantly between sober and drunk states, the investigators assume that different driving patterns in both states can be used to generate drunk driving detection models using machine learning neural networks (deep machine learning classifiers).

Eligibility criteria: Inclusion Criteria:

* Informed consent as documented by signature.
* In possession of a Swiss or EU driving license for at least two years.
* At least driving 1'000 kilometers per year.
* No special equipment needed when driving.
* Drinks alcohol at least occasionally (moderate/social consumption).
* Fluent in (Swiss) German and no speech impairment.
* Lives in or near Bern.

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 PEth in capillary blood \> 210 ng/mL at first visit.
* Known or suspected non-compliance or drug abuse.
* 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: Accuracy of the DRIVE-model: Diagnostic accuracy of the drunk driving warning system (DRIVE) to detect drunk driving (>= 0.25 mg/l breath alcohol concentration (BrAC)) quantified as the area under the receiver operator characteristics curve (AUC ROC).
timeFrame: 480 minutes
Secondary outcomes:
measure: Change of velocity
timeFrame: 480 minutes
measure: Change of steer
timeFrame: 480 minutes
measure: Change of brake
timeFrame: 480 minutes
measure: Change of steer torque
timeFrame: 480 minutes
measure: Change of steer speed
timeFrame: 480 minutes
measure: Change of time driving over midline
timeFrame: 480 minutes
measure: Change of swerving
timeFrame: 480 minutes
measure: Change of spinning
timeFrame: 480 minutes
measure: Change of gaze behavior
timeFrame: 480 minutes
measure: Change of gaze regions of interest
timeFrame: 480 minutes
measure: Change of gaze events
timeFrame: 480 minutes
measure: Defining the alcohol concentration when driving performance is decreased
timeFrame: 480 minutes
measure: Driving performance while being sober, above and within the legal limit
timeFrame: 480 minutes
measure: Change of heart-rate
timeFrame: 480 minutes
measure: Change of heart-rate variability
timeFrame: 480 minutes
measure: Change of wrist accelerometer
timeFrame: 480 minutes
measure: Change of electrodermal activity (EDA)
timeFrame: 480 minutes
measure: Change of skin temperature
timeFrame: 480 minutes
measure: Change of oral fluid cortisol
timeFrame: 2 weeks
measure: Change of phosphatidylethanol (PEth) in capillary blood
timeFrame: 2 weeks
measure: Change of ethylglucuronide (EtG) in capillary blood
timeFrame: 2 weeks
measure: Change of ethylsulfate (EtS) in capillary blood
timeFrame: 2 weeks
measure: Change of cortisol in capillary blood
timeFrame: 2 weeks
measure: Accuracy-comparison of DRIVE-model and DRIVEplus-model
timeFrame: 480 minutes
measure: Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator characteristics curve using physiological data
timeFrame: 480 minutes
measure: Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using video data
timeFrame: 480 minutes
measure: Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using eye-tracking data
timeFrame: 480 minutes
measure: Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using audio data
timeFrame: 480 minutes
measure: Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator curve (AUC-ROC) using radar sensor data
timeFrame: 480 minutes
measure: Self-estimation of alcohol concentrations
timeFrame: 480 minutes
measure: Self-estimation of driving performance
timeFrame: 480 minutes
measure: Self-estimation of workload
timeFrame: 480 minutes
measure: Self-estimation of sleepiness levels
timeFrame: 480 minutes
measure: Incidence of Adverse Events (AEs)
timeFrame: 3 weeks
measure: Incidence of Serious Adverse Events (SAEs)
timeFrame: 3 weeks
Other outcomes:
Locations:
city: Bern
country: Switzerland
facility: University of Bern
Study officials:
affiliation: University of Bern
name: Wolfgang Weinmann, Prof. Dr.
role: PRINCIPAL_INVESTIGATOR
References: