A Novel Approach to Detect Drowsy Driving Resulting From Sleep Loss and Sleep Pressure

睡眠不足已成为一种现代流行病，对日间功能产生重大影响，在美国每年因困倦驾驶导致约6000起致命车祸，在加拿大每年约100起。一个潜在的危及生命的结果是增加驾驶员疲劳，往往导致严重的机动车事故。拟议研究的主要目标是开发一种非侵入性，实时和非侵入性的车载系统，以检测驾驶员疲劳之前，机动车事故发生。我们提出的系统使用红外线（IR）眼动跟踪摄像机技术来测量困倦之前的眼动。这些眼球运动通过我们新颖的人工智能算法在线分类，以检测嗜睡的先兆，用于在驾驶时提醒驾驶员。在这里，使用驾驶模拟技术，我们的目标是研究眼动特性和行为和电生理警告信号的驾驶员即将困倦之间的关系。这将使我们能够开发一种方法来检测困倦，并在判断出现障碍和失误之前提醒驾驶员。我们计划开发一种新的方法，仅使用眼动跟踪数据有效地检测困倦：1）不同的睡眠压力，以及2）白天或夜晚不同时间困倦的自然变化。最终，这将导致“预警系统”的商业化，作为运输卡车行业、公共交通和个人乘用车中嗜睡损害的对策。我们希望这项工作将开辟进一步的途径，研究和应用有关的健身驾驶由于各种其他原因，如酒精，药物使用和分心驾驶。

Sleep loss has become a modern-day epidemic, with a significant impact on daytime functioning, resulting in about 6000 fatal car crashes/year due to drowsy driving in the US and about 100/year in Canada. One potentially life-threatening outcome is increased driver fatigue, often resulting in serious motor vehicle accidents. The main objective of the proposed research is the development of a non-invasive, real-time and unobtrusive in-vehicle system to detect driver fatigue before a motor vehicle accident occurs. Our proposed system measures eye movements that precede drowsiness using infra-red (IR) eye tracking camera technology. These eye movements are classified online by our novel artificial intelligence algorithms to detect the antecedents of somnolence, to be used to alert the driver while driving. Here, using driving simulation technology, we aim to study the relationship between eye movement characteristics and the behavioural and electrophysiological warning signs of impending driver sleepiness. This will allow us to develop a method to detect sleepiness and alert drivers before impairment and lapses in judgement occur. We plan to develop a novel method to effectively detect drowsiness using only eye tracking data in: 1) varying sleep-pressure, and, 2) natural variations in drowsiness at different times of day or night. Ultimately, this will result in the commercialization of an "early warning system", as a countermeasure to drowsiness impairment in the transport-trucking industry, public transit and in individual passenger vehicles. We expect that this work will open up further avenues for research and applications about the fitness to drive due to various other causes, such as alcohol, drug use and distracted driving.