Non-randomised, Controlled, Interventional Single-centre Study for the Design and Evaluation of an In-vehicle Drunk Driving Detection System
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 30
- 试验地点
- 1
- 主要终点
- 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).
研究概览
简要总结
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).
详细描述
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).
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 17 Years 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
排除标准
- •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.
研究组 & 干预措施
Intervention group
Intervention: Other: Driving under the influence of alcohol with a driving simulator
干预措施: Driving under the influence of alcohol with a driving simulator (Other)
结局指标
主要结局
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).
时间窗: 480 minutes
Accuracy of the DRIVE-model will be assessed using driving data recorded in sober and drunk driving states and driving data will be analysed using applied machine learning technology for impaired driving detection.
次要结局
- Change of ethylglucuronide (EtG) in capillary blood(2 weeks)
- 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(480 minutes)
- 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(480 minutes)
- Incidence of Adverse Events (AEs)(3 weeks)
- Change of velocity(480 minutes)
- Change of brake(480 minutes)
- Change of heart-rate variability(480 minutes)
- Change of wrist accelerometer(480 minutes)
- Change of spinning(480 minutes)
- Defining the alcohol concentration when driving performance is decreased(480 minutes)
- Change of steer speed(480 minutes)
- Change of time driving over midline(480 minutes)
- Change of steer(480 minutes)
- Change of steer torque(480 minutes)
- Change of gaze regions of interest(480 minutes)
- Change of heart-rate(480 minutes)
- 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(480 minutes)
- Change of swerving(480 minutes)
- Change of gaze behavior(480 minutes)
- Driving performance while being sober, above and within the legal limit(480 minutes)
- Change of electrodermal activity (EDA)(480 minutes)
- Change of skin temperature(480 minutes)
- Change of cortisol in capillary blood(2 weeks)
- Accuracy-comparison of DRIVE-model and DRIVEplus-model(480 minutes)
- Change of gaze events(480 minutes)
- Change of oral fluid cortisol(2 weeks)
- Change of ethylsulfate (EtS) in capillary blood(2 weeks)
- Change of phosphatidylethanol (PEth) in capillary blood(2 weeks)
- Diagnostic accuracy in detecting drunk driving (>= 0.25 mg/l BrAC) quantified as the area under the receiver operator characteristics curve using physiological data(480 minutes)
- 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(480 minutes)
- Self-estimation of alcohol concentrations(480 minutes)
- Self-estimation of driving performance(480 minutes)
- Self-estimation of workload(480 minutes)
- Self-estimation of sleepiness levels(480 minutes)
- Incidence of Serious Adverse Events (SAEs)(3 weeks)