Alcohol Drinking, Drunk Driving, Impaired Driving
Conditions
Keywords
Driving, Automotive Technology, Car, Alcohol Biomarker, Test Track
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).
Interventions
OTHERDriving 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
OTHERDriving 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
Sponsors
University of Bern
ETH Zurich
University of St.Gallen
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
OTHER
Masking
SINGLE (Subject)
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).
Intervention model description
Treatment group (32 participants); Reference group (12 participants); Placebo group (12 participants)
Eligibility
Sex/Gender
ALL
Age
21 Years to No maximum
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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) | 480 minutes | 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 | 480 minutes | 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 | 480 minutes | 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 | 480 minutes | 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 | 480 minutes | 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 | 480 minutes | 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 | 480 minutes | Steering is recorded based on the controller area network. |
| Change of steer torque over the alcohol intoxication trajectory | 480 minutes | Steer torque is recorded based on the controller area network. |
| Change of steer speed over the alcohol intoxication trajectory | 480 minutes | Steer speed is recorded based on the controller area network. |
| Change of velocity over the alcohol intoxication trajectory | 480 minutes | Velocity is recorded based on the controller area network. |
| Change of acceleration over the alcohol intoxication trajectory | 480 minutes | Acceleration is recorded based on the controller area network. |
| Change of braking over the alcohol intoxication trajectory | 480 minutes | Braking is recorded based on the controller area network. |
| Change of swerving over the alcohol intoxication trajectory | 480 minutes | Swerving is recorded based on the controller area network. |
| Change of spinning over the alcohol intoxication trajectory | 480 minutes | Spinning is recorded based on the controller area network. |
| Change of gaze position over the alcohol intoxication trajectory | 480 minutes | Gaze position is recorded using an eye-tracker device. |
| Change of gaze velocity over the alcohol intoxication trajectory | 480 minutes | Gaze velocity is recorded using an eye-tracker device. |
| Change of gaze acceleration over the alcohol intoxication trajectory | 480 minutes | Gaze acceleration is recorded using an eye-tracker device. |
| Change of gaze regions of interest over the alcohol intoxication trajectory | 480 minutes | 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 | 480 minutes | Gaze events (e.g., fixations, saccades, etc.) are recorded using an eye-tracker device. |
| Change of head pose over the alcohol intoxication trajectory | 480 minutes | Head pose (position/rotation) is recorded using an eye-tracker device. |
| Change of heart rate over the alcohol intoxication trajectory | 480 minutes | Heart rate is recorded using a heart rate monitoring device and wearables. |
| Change of heart rate variability over the alcohol intoxication trajectory | 480 minutes | Heart rate variability is recorded using a heart rate monitoring device and wearables. |
| Change of electrodermal activity over the alcohol intoxication trajectory | 480 minutes | Electrodermal activity is recorded using wearables. |
| Change of wrist accelerometer measurements over the alcohol intoxication trajectory | 480 minutes | Wrist accelerometer measurements are recorded using wearables. |
| Change of skin temperature over the alcohol intoxication trajectory | 480 minutes | Skin temperature is recorded using wearables. |
| Self-assessment of driving performance over the alcohol intoxication trajectory | 480 minutes | 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 | 480 minutes | Participants estimate their blood alcohol concentration. |
| Number of driving mishaps over the alcohol intoxication trajectory | 480 minutes | Any driving mishaps, accidents and interventions by the driving instructor will be documented. |
| Number of Adverse Events (AEs) | 3 months, from screening to close out visit for each participant | Adverse Events will be recorded at each study visit. |
| Number of Serious Adverse Events (SAEs) | 3 months, from screening to close out visit for each participant. | Serious Adverse Events will be recorded at each study visit. |
Countries
Switzerland
Contacts
PRINCIPAL_INVESTIGATORWolfgang Weinmann, Prof. Dr.
University of Bern
Outcome results
None listed