Design and Evaluation of an In-Vehicle Real-Time Drunk Driving Detection System

Alcohol Drinking Clinical Trial

— 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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05796609
• Other study ID #: DRIVE Test Track
• Secondary ID: SNCTP000005396
• Status: Completed
• Phase: N/A
• Start date: April 5, 2023
• Est. completion date: August 1, 2023

Study information

• Verified date: November 2023
• Source: University of Bern
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical 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.

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).

Recruitment information / eligibility

• Status: Completed
• Enrollment: 55
• Est. completion date: August 1, 2023
• Est. primary completion date: August 1, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 21 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Alcohol Drinking
• Drunk Driving
• Impaired Driving

Intervention

Other:
• Driving under the influence of alcohol
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 placebo
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

Locations

Country	Name	City	State
Switzerland	Institut für Rechtsmedizin	Bern

Sponsors (3)

Lead Sponsor	Collaborator
University of Bern	ETH Zurich, University of St.Gallen

Country where clinical trial is conducted

Switzerland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	480 minutes
Secondary	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.	480 minutes
Secondary	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.	480 minutes
Secondary	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.	480 minutes
Secondary	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.	480 minutes
Secondary	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.	480 minutes
Secondary	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.	480 minutes
Secondary	Change of steering over the alcohol intoxication trajectory	Steering is recorded based on the controller area network.	480 minutes
Secondary	Change of steer torque over the alcohol intoxication trajectory	Steer torque is recorded based on the controller area network.	480 minutes
Secondary	Change of steer speed over the alcohol intoxication trajectory	Steer speed is recorded based on the controller area network.	480 minutes
Secondary	Change of velocity over the alcohol intoxication trajectory	Velocity is recorded based on the controller area network.	480 minutes
Secondary	Change of acceleration over the alcohol intoxication trajectory	Acceleration is recorded based on the controller area network.	480 minutes
Secondary	Change of braking over the alcohol intoxication trajectory	Braking is recorded based on the controller area network.	480 minutes
Secondary	Change of swerving over the alcohol intoxication trajectory	Swerving is recorded based on the controller area network.	480 minutes
Secondary	Change of spinning over the alcohol intoxication trajectory	Spinning is recorded based on the controller area network.	480 minutes
Secondary	Change of gaze position over the alcohol intoxication trajectory	Gaze position is recorded using an eye-tracker device.	480 minutes
Secondary	Change of gaze velocity over the alcohol intoxication trajectory	Gaze velocity is recorded using an eye-tracker device.	480 minutes
Secondary	Change of gaze acceleration over the alcohol intoxication trajectory	Gaze acceleration is recorded using an eye-tracker device.	480 minutes
Secondary	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.	480 minutes
Secondary	Change of gaze events over the alcohol intoxication trajectory	Gaze events (e.g., fixations, saccades, etc.) are recorded using an eye-tracker device.	480 minutes
Secondary	Change of head pose over the alcohol intoxication trajectory	Head pose (position/rotation) is recorded using an eye-tracker device.	480 minutes
Secondary	Change of heart rate over the alcohol intoxication trajectory	Heart rate is recorded using a heart rate monitoring device and wearables.	480 minutes
Secondary	Change of heart rate variability over the alcohol intoxication trajectory	Heart rate variability is recorded using a heart rate monitoring device and wearables.	480 minutes
Secondary	Change of electrodermal activity over the alcohol intoxication trajectory	Electrodermal activity is recorded using wearables.	480 minutes
Secondary	Change of wrist accelerometer measurements over the alcohol intoxication trajectory	Wrist accelerometer measurements are recorded using wearables.	480 minutes
Secondary	Change of skin temperature over the alcohol intoxication trajectory	Skin temperature is recorded using wearables.	480 minutes
Secondary	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).	480 minutes
Secondary	Self-estimation of alcohol concentrations over the alcohol intoxication trajectory	Participants estimate their blood alcohol concentration.	480 minutes
Secondary	Number of driving mishaps over the alcohol intoxication trajectory	Any driving mishaps, accidents and interventions by the driving instructor will be documented.	480 minutes
Secondary	Number of Adverse Events (AEs)	Adverse Events will be recorded at each study visit.	3 months, from screening to close out visit for each participant
Secondary	Number of Serious Adverse Events (SAEs)	Serious Adverse Events will be recorded at each study visit.	3 months, from screening to close out visit for each participant.