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:过度酒精消费导致残疾和死亡。数字干预是促进行为变化,从而预防与酒精有关的伤害的有希望的手段,特别是在驾驶等关键时刻。这需要实时了解一个人血液酒精浓度(BAC)的信息。在这里,我们开发了一个汽车机学习系统,以预测严重酒精酒精浓度(BAC)水平。我们的系统利用了全世界许多国家授权的驾驶员监控摄像机。我们用干预模拟器研究的30名参与者对我们的系统进行了评估。我们的系统可靠地检测了任何酒精影响下的驾驶(接收器操作特征曲线(AUROC) 0.88之下的地区)以及超过世界卫生组织建议的0.05g/dL BAC限制的驾驶(AUROC 0.79)的情况。示范检查表明依赖与酒精消费相关的病理学影响。据我们所知,我们是第一个严格评估使用驾驶员监控摄像机检测醉酒驾驶的系统。我们的结果突出了驾驶员监控摄像机的潜力,并使得下一代醉酒驾驶员的互动能够防止与酒精有关的伤害。