Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information on a person's blood alcohol concentration (BAC). Here, we develop an in-vehicle machine learning system to predict critical BAC levels. Our system leverages driver monitoring cameras mandated in numerous countries worldwide. We evaluate our system with n=30 participants in an interventional simulator study. Our system reliably detects driving under any alcohol influence (area under the receiver operating characteristic curve [AUROC] 0.88) and driving above the WHO recommended limit of 0.05g/dL BAC (AUROC 0.79). Model inspection reveals reliance on pathophysiological effects associated with alcohol consumption. To our knowledge, we are the first to rigorously evaluate the use of driver monitoring cameras for detecting drunk driving. Our results highlight the potential of driver monitoring cameras and enable next-generation drunk driver interaction preventing alcohol-related harm.

翻译：过量饮酒导致残疾与死亡。数字干预措施是促进行为改变、预防酒精相关危害（尤其是在驾驶等关键时刻）的有效手段。这需要实时获取个体的血液酒精浓度（BAC）信息。本文开发了一种车载机器学习系统，用于预测关键BAC水平。该系统利用了全球多国强制配备的驾驶员监控摄像头。我们在包含30名参与者的干预性模拟器研究中评估了该系统。系统可可靠检测任何酒精影响下的驾驶行为（受试者工作特征曲线下面积[AUROC]为0.88），以及驾驶时BAC超过世界卫生组织建议限值（0.05g/dL）的情况（AUROC为0.79）。模型分析显示其依赖与酒精消费相关的病理生理效应。据我们所知，这是首次系统评估利用驾驶员监控摄像头检测醉酒驾驶的研究。结果凸显了驾驶员监控摄像头的潜力，并为下一代预防酒精相关危害的醉酒驾驶员交互系统奠定了基础。