Dataset "FULL" for Drowsiness Detection in Drivers

Drowsiness is an intermediate condition that fluctuates between alertness and sleep. It reduces the consciousness level and hinders a person from responding quickly to important road safety issues [1]. The American Automobile Association (AAA) has reported that about 24% of 2,714 drivers that participated in a survey revealed being extremely drowsy while driving, at least once in the last month [2]. In 2017, the National Highway Transportation Safety Administration (NHTSA) also reported 795 fatalities in motor vehicle crashes involving drowsy drivers [3]. Drowsy driving has caused about 2.5% of fatal accidents from 2011 through 2015 in the USA, and it is estimated to produce an economic loss of USD 230 billion annually [4]. Klauer et al. have found in their study that drowsy drivers contributed to 22-24% of crashes or near-crash risks [5]. The German Road Safety Council (DVR) has reported that one out of four fatal highway crashes has been caused by drowsy drivers [6]. In a study carried out in 2015, it has been reported that the average prevalence of falling asleep while driving in the previous two years was about 17% in 19 European countries [6]. The results of these studies emphasize the importance of detecting drowsiness early enough to initiate preventive measures. Drowsiness detection systems are intended to warn the drivers before an upcoming level of drowsiness gets critical to prevent drowsiness-related accidents.Intelligent Systems that automate motor vehicle driving on the roads are being introduced to the market step-wise. The Society of Automotive Engineers (SAE) issued a standard defining six levels ranging from no driving automation (level 0) to full driving automation (level 5) [7]. While the SAE levels 0-2 require that an attentive driver carries out or at least monitors the dynamic driving task, in the SAE level 3 of automated driving, drivers will be allowed to do a secondary task allowing the system to control the vehicle under limited conditions, e.g., on a motorway. Still, the automation system has to hand back the vehicle guidance to the driver whenever it cannot control the state of the vehicle any more. However, the handover of vehicle control to a drowsy driver is not safe. Therefore, the system should be informed about the state of the driver.To date, different Advanced Driver Assistance Systems (ADAS) have been made by car manufactures and researchers to improve driving safety and manage the traffic flow. ADAS systems have been benefited from advanced machine perception methods, improved computing hardware systems, and intelligent vehicle control algorithms. By recently increasing the availability of huge amounts of sensor data to ADAS, data-driven approaches are extensively exploited to enhance their performance. The driver drowsiness detection systems have gained much attention from researchers. Before its use in the development of driving automation, drowsiness warning systems have been produced for the direct benefit of avoiding accidents.The aim of the WACHSens project was to collect a big data set to detect the different levels of driver drowsiness during performing two different driving modes: manual and automated. References:[1]    M. Awais, N. Badruddin, and M. Drieberg, "A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability,"Sensors, vol. 17, no. 9, 2017, doi: 10.3390/s17091991 [2]   AAA Foundation for Traffic Safety, "2019 Traffic Safety Culture Index (Technical Report), June 2020," Washington, D.C., Jun. 2020. [Online]. Available: https://aaafoundation.org/2019-traffic-safety-culture-index/[3]   National Highway Traffic Safety Administration, "Traffic Safety Facts: 2017 Fatal Motor Vehicle Crashes: Overview," NHTSA's National Center for Statistics and Analysis, 1200 New Jersey Avenue SE., Washington DOT HS 812 603, Oct. 2018. Accessed: Apr. 14 2021. [Online]. Available: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812603[4]   Agustina Garcés Correa, Lorena Orosco, and Eric Laciar, "Automatic detection of drowsiness in EEG records based on multimodal analysis," Medical Engineering & Physics, vol. 36, no. 2, pp. 244–249, 2014, doi: 10.1016/j.medengphy.2013.07.011[5]   S. Klauer, V. Neale, T. Dingus, Jeremy Sudweeks, and D. J. Ramsey, "The Prevalence of Driver Fatigue in an Urban Driving Environment : Results from the 100-Car Naturalistic Driving Study," in 2006.[6]   Fraunhofer-Gesellschaft,Eyetracker warns against momentary driver drowsiness - Press Release Oktober 12, 2010. [Online]. Available: https://www.fraunhofer.de/en/press/research-news/2010/10/eye-tracker-driver-drowsiness.html (accessed: Apr. 14 2021).[7]   T. Inagaki and T. B. Sheridan, "A critique of the SAE conditional driving automation definition, and analyses of options for improvement," Cogn Tech Work, vol. 21, no. 4, pp. 569–578, 2019, doi: 10.1007/s10111-018-0471-5

嗜睡是介于清醒与睡眠之间的一种波动状态，会降低意识水平，妨碍驾驶员对道路交通安全相关的重要问题做出快速响应[1]。美国汽车协会（American Automobile Association, AAA）的一项调查显示，在2714名受访驾驶员中，约24%的人表示在过去一个月内至少有一次在驾驶时处于极度嗜睡状态[2]。2017年，美国国家公路交通安全管理局（National Highway Transportation Safety Administration, NHTSA）报告称，涉及嗜睡驾驶员的机动车碰撞事故共造成795人死亡[3]。2011至2015年间，美国约2.5%的致命交通事故由驾驶时嗜睡引发，据估算每年由此造成的经济损失高达2300亿美元[4]。Klauer等人的研究发现，嗜睡驾驶员导致了22%~24%的碰撞或临界碰撞风险[5]。德国道路安全委员会（German Road Safety Council, DVR）报告称，每4起致命高速公路碰撞事故中就有1起由嗜睡驾驶员引发[6]。2015年的一项研究显示，在19个欧洲国家中，过去两年内曾在驾驶时睡着的驾驶员平均占比约为17%[6]。上述研究结果均强调了及早检测嗜睡状态并采取预防措施的重要性。嗜睡检测系统旨在在嗜睡程度达到临界值前向驾驶员发出预警，以避免与嗜睡相关的交通事故。

当前，能够在道路上实现机动车自动驾驶的智能系统正逐步推向市场。美国汽车工程师学会（Society of Automotive Engineers, SAE）发布了一项标准，将自动驾驶等级划分为从无驾驶自动化（等级0）到完全驾驶自动化（等级5）共6个等级[7]。SAE等级0至2要求驾驶员保持专注，执行或至少监控动态驾驶任务；而在SAE等级3的自动驾驶场景中，驾驶员可从事次要任务，系统可在有限条件下（例如高速公路上）控制车辆。但当系统无法再控制车辆状态时，需将车辆操控权交还给驾驶员。然而，将车辆控制权移交至嗜睡驾驶员并不安全，因此系统需要获取驾驶员的状态信息。

迄今为止，汽车制造商与研究人员已开发出多种先进驾驶辅助系统（Advanced Driver Assistance Systems, ADAS），以提升驾驶安全性并优化交通流管理。ADAS系统得益于先进的机器感知技术、升级的计算硬件系统以及智能车辆控制算法。近年来，随着ADAS可获取的传感器数据量大幅提升，数据驱动方法被广泛用于提升系统性能。驾驶员嗜睡检测系统已受到研究者的广泛关注。在自动驾驶技术研发应用之前，嗜睡预警系统便已被开发出来，用于直接避免交通事故的发生。

WACHSens项目的目标是构建一个大规模数据集，用于检测驾驶员在两种不同驾驶模式——手动驾驶与自动驾驶——下的不同嗜睡程度。

参考文献：
[1] M. Awais、N. Badruddin与M. Drieberg，《利用生理信号检测驾驶员嗜睡以提升系统性能与可穿戴性的混合方法》，《传感器》，第17卷，第9期，2017年，DOI: 10.3390/s17091991
[2] 美国汽车协会交通安全基金会，《2019年交通安全文化指数（技术报告），2020年6月》，华盛顿哥伦比亚特区，2020年6月。[在线资源]。可访问：https://aaafoundation.org/2019-traffic-safety-culture-index/
[3] 美国国家公路交通安全管理局，《交通安全事实：2017年致命机动车碰撞事故：概述》，NHTSA国家统计分析中心，新泽西大道东南1200号，华盛顿 HS 812 603，2018年10月。访问日期：2021年4月14日。[在线资源]。可访问：https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812603
[4] Agustina Garcés Correa、Lorena Orosco与Eric Laciar，《基于多模态分析的脑电图（Electroencephalogram, EEG）记录中驾驶员嗜睡自动检测》，《医学工程与物理学》，第36卷，第2期，第244-249页，2014年，DOI: 10.1016/j.medengphy.2013.07.011
[5] S. Klauer、V. Neale、T. Dingus、Jeremy Sudweeks与D. J. Ramsey，《城市驾驶环境下驾驶员疲劳的流行率：100车自然驾驶研究结果》，2006年。
[6] 弗劳恩霍夫协会，《眼动追踪器可预警驾驶员瞬时嗜睡——2010年10月12日新闻稿》。[在线资源]。可访问：https://www.fraunhofer.de/en/press/research-news/2010/10/eye-tracker-driver-drowsiness.html（访问日期：2021年4月14日）。
[7] T. Inagaki与T. B. Sheridan，《对SAE有条件自动驾驶定义的批判及改进方案分析》，《认知技术与工作》，第21卷，第4期，第569-578页，2019年，DOI: 10.1007/s10111-018-0471-5