地铁驾驶员驾驶疲劳状态监测

为监测地铁自动驾驶系统驾驶模式下驾驶员驾驶疲劳状态,以S地铁公司的驾驶员为研究对象,开展驾驶员疲劳主、客观监测研究。主观监测应用《自觉症状调查表》调查并统计分析地铁驾驶员的驾驶主观疲劳感受;客观监测应用Eegosports 64通道无线脑电肌电系统测量地铁驾驶员在各班次、各时间段的脑电(EEG)信号,并结合Matlab工具箱中的EEGLAB分析各班次驾驶员EEG中δ波的频谱图。结果表明:驾驶员驾驶疲劳总体的平均得分为1.8分,即驾驶疲劳有些明显,且晚班和夜班驾驶疲劳比白班的大,从主客观2方面说明驾驶员处于疲劳驾驶状态。     

基于眼-脑生理指标的大型地下洞室驾驶员疲劳演化特征研究

为保障大型地下洞室驾驶安全，提出眼动和脑电双模态驱动的大型地下洞室驾驶疲劳评价方法，探究驾驶员疲劳演化特征。首先，根据实地数据设计大型地下洞室模拟场景，利用驾驶模拟技术开展驾驶试验，实时采集驾驶员的眼动和脑电数据；其次，对数据进行分段处理，基于格拉布斯准则剔除眼动异常数据，通过快速傅里叶变换分解出脑电节律，构建脑电疲劳指数模型；最后，开展不同区段下驾驶员平均瞳孔直径、眨眼持续时间、眨眼频率及θ、α、β节律等指标的差异性分析，以平均瞳孔直径和脑电疲劳指标F为参量，提出基于模糊综合评价的驾驶疲劳度量方法。结果表明，驾驶员的视觉疲劳出现明显早于精神疲劳，而精神疲劳可以更精确地体现影响驾驶状态的内在疲劳。相较于地上路段，驾驶员在大型地下洞室中的疲劳累积更快，呈现反复、波动式上升，且其综合疲劳程度在中后段达到峰值，之后受洞口光亮刺激在末段减弱。     

高原公路低氧路段驾驶疲劳脑电信号检测分析

为了解驾驶员在高原低氧路段的疲劳程度,以寻求缓解驾驶疲劳提高行车安全的途径,利用生物反馈检测仪分别对初次与经常进入高原公路低氧路段的驾驶员进行实地行车试验。通过对比不同海拔高度受测驾驶员脑电(EEG)变化特征,选取脑电8~13频段与14~30频段的平均功率比值R作为评价驾驶员疲劳时脑电特性指标R,定量分析海拔、连续驾驶时间对R的影响,同时建立R与海拔、连续驾驶时间之间的关系模型。研究表明:海拔与连续驾驶时间是影响驾驶员疲劳的主要因素,R随着海拔的升高与连续驾驶时间的增长而逐渐变大。初次在高原低氧路段行车的驾驶员表现出的疲劳感强于经常在高原低氧路段行车的驾驶员。     

高速公路驾驶员主动疲劳的脑电检测分析

为开发高速公路驾驶疲劳预警系统,保障道路交通安全,基于脑电(EEG)数据功率谱分析方法,探索驾驶员主动疲劳与脑电指标(θ+α)/β的关系,首先,开展模拟驾驶试验,采集21名被试驾驶状态的脑电信号,分析α(8～13 Hz),β(13～30 Hz),θ(0. 5～4 Hz)这3个频段的脑电波,计算脑电合并指标(θ+α)/β;然后,运用瑞典行业疲劳问卷(SOFI),比较驾驶员执行驾驶任务前后的疲劳状态,分析心理测量和脑电测量结果的回归拟合效度。结果表明:在高速公路复杂驾驶任务中,驾驶员脑电合并指标(θ+α)/β呈现下降趋势,同时,(θ+α)/β与驾驶员主观疲劳程度有显著的正向拟合关系,拟合解释率达50%;脑电指标(θ+α)/β可实时预测驾驶员主动疲劳状态。     

草原公路短时程驾驶疲劳程度多指标划分研究

为确定行车过程中不同时段驾驶员的疲劳程度,考虑草原公路的特殊性,选取典型草原公路路段,对9位受试者腰部肌电(EMG)、脑电(EEG)及心电(ECG)信号进行连续3 h的实驾测试。用因子(降维)及相关性分析法得到疲劳敏感指标;通过回归方程得到疲劳公式;利用层次聚类法初步划分疲劳程度,并验证划分结果。试验结果表明:表征EMG信号频谱变化的中位频率(MF)、ECG信号的心率均值(MHR)、高频标准化值(HFnu)和EEG信号的(α+θ)/β值对草原公路驾驶疲劳响应敏感,且驾驶疲劳发展呈多元线性变化;草原公路短时程驾驶疲劳可分为3个阶段。     

基于驾驶操作行为的驾驶员疲劳状态识别模型研究

以驾驶疲劳状态监测为研究对象,介绍现有几种疲劳检测方法及其优缺点,提出把驾驶行为操作和驾驶员生理指标相结合建立疲劳识别模型的思想。通过大量模拟器驾驶实验,建立驾驶操作和驾驶员生理指标之间的关系模型,并运用最小二乘法对数学模型进行了参数识别。利用驾驶员生理指标能较好判别驾驶员状态特性的特点,找出驾驶操作行为和驾驶状态之间的关系。研究结果有助于建立驾驶操作行为和驾驶员疲劳状态之间的关系模型。     

基于脑电信号的驾驶员情绪状态识别研究

为研究驾驶员情绪状态识别技术,利用特定题材的影音氛围诱发兴奋和悲伤2种情绪,结合汽车模拟驾驶试验采集驾驶员情绪化驾驶时的脑电信号,利用db5小波分解算法分解信号,提取受情绪状态显著的脑电信号成分,并计算相应的功率谱。结果表明:驾驶员左右额叶区脑电β波受情绪状态影响显著;平静状态时,驾驶员左右额叶脑电β波功率值Pβ大小近似,数值较小;兴奋状态时,左右额叶脑电Pβ大小近似,相比于平静时显著增加;悲伤状态时,左额叶脑电Pβ显著增加,右额叶脑电Pβ相比于平静时无显著变化。Pβ可作为区分驾驶员平静(平常)、兴奋和悲伤这3种情绪状态的依据。     

疲劳影响驾驶员心理旋转能力的ERP试验研究

为探究疲劳对驾驶员心理旋转能力的影响机制,首先开展2 h模拟驾驶任务试验,以诱发驾驶疲劳,并在模拟驾驶任务前后,分别测定驾驶员的心理旋转能力;然后基于试验数据,分析驾驶疲劳前后心理旋转能力行为绩效(反应时间、正确率)和脑电事件相关电位(ERP)成分(P3波幅和潜伏期)的变化及其差异。试验发现,驾驶疲劳引起行为绩效显著降低(反应时延长,正确率降低),脑电ERP的P3成分波幅在顶叶区显著下降、潜伏期显著延长。上述结果表明:疲劳影响了驾驶员在心理旋转过程中对认知资源的分配和加工信息的速度,导致心理旋转能力降低。     

车载驾驶员疲劳驾驶预警与控制系统研究

为了识别与安全控制疲劳危险驾驶行为,有效预防与减少因疲劳危险驾驶导致的交通事故,基于疲劳检测分级、预警与自动智能控制技术,开发出车载驾驶员疲劳驾驶实时监测预警与控制系统。首先基于PERCLO方法,构建驾驶员疲劳检测与分级模型;然后根据所输出的驾驶员疲劳等级信息,提出疲劳驾驶三级预警原理,及其预警实现方式;最后以疲劳预警信息为基础,形成基于驾驶员不同疲劳等级预警的安全控制技术,并对处于深度疲劳的危险驾驶行为,构建自动智能紧急控制停车系统,重点阐述系统的地形匹配、智能控制,以及自动驾驶与停车三大核心技术,并提出相应的系统硬件构成,为系统工程应用提供理论与技术支持。     

基于生理信号的驾驶疲劳综合指标试验研究

研究驾驶过程中随着疲劳的产生驾驶员生理信号的变化规律,提取反映驾驶疲劳程度的综合指标。采用驾驶模拟器对20名被试进行驾驶模拟试验,用MP150多导生理仪实时采集并记录驾驶员在60 min驾驶任务过程中的心电信号、脑电信号、肌肉电阻信号、皮肤温度信号和呼吸频率信号。运用R软件对数据进行线性回归分析,对比一般回归分析,逐步线性回归分析克服了一般回归分析许多变量不显著的缺点,得到了最优的驾驶综合指标方程,确定了与各项指标相关的驾驶疲劳评价综合指标,并通过3名被试模拟驾驶试验验证了综合指标作为评价驾驶疲劳的有效性。     

Age-related differences in effects of non-driving related tasks on takeover performance in automated driving

Introduction: During SAE level 3 automated driving, the driver’s role changes from active driver to fallback-ready driver. Drowsiness is one of the factors that may degrade driver’s takeover performance. This study aimed to investigate effects of non-driving related tasks (NDRTs) to counter driver’s drowsiness with a Level 3 system activated and to improve successive takeover performance in a critical situation. A special focus was placed on age-related differences in the effects. Method: Participants of three age groups (younger, middle-aged, older) drove the Level 3 system implemented in a high-fidelity motion-based driving simulator for about 30 min under three experiment conditions: without NDRT, while watching a video clip, and while switching between watching a video clip and playing a game. The Karolinska Sleepiness Scale and eyeblink duration measured driver drowsiness. At the end of the drive, the drivers had to take over control of the vehicle and manually change the lane to avoid a collision. Reaction time and steering angle variability were measured to evaluate the two aspects of driving performance. Results: For younger drivers, both single and multiple NDRT engagements countered the development of driver drowsiness during automated driving, and their takeover performance was equivalent to or better than their performance without NDRT engagement. For older drivers, NDRT engagement did not affect the development of drowsiness but degraded takeover performance especially under the multiple NDRT engagement condition. The results for middle-aged drivers fell at an intermediate level between those for younger and older drivers. Practical Applications: The present findings do not support general recommendations of NDRT engagement to counter drowsiness during automated driving. This study is especially relevant to the automotive industry’s search for options that will ensure the safest interfaces between human drivers and automation systems.     

生理疲劳和心理疲劳对车辆驾驶的影响对比

针对现有疲劳驾驶预警和干预技术研究鲜有对生理疲劳和心理疲劳进行区分考虑的问题,为对比这两类典型疲劳态对驾驶员车辆驾驶过程的事故倾向影响,分别从性别、年龄和驾龄的角度分3批次共招募90位驾驶员进行状态诱发和驾驶实验。结果表明:尽管生理疲劳和心理疲劳都会如传统研究所述导致各驾驶员的驾驶违规倾向增加和驾驶能力降低,但是二者对于各类别驾驶员的驾驶影响程度和规律存在差异甚至迥异。研究疲劳驾驶相关问题时有必要首先判断驾驶员是生理疲劳还是心理疲劳,这是一个被普遍忽视而又可能影响研究结论准确性和有效性的重要因素。     

Drowsiness and distraction while driving: A study based on smartphone app data

Introduction: Due to the negative impact on road safety from driver drowsiness and distraction, several studies have been conducted, usually under driving simulator and naturalistic conditions. Nevertheless, emerging technologies offer the opportunity to explore novel data. The present study explores retrospective data, which was gathered by an app designed to monitor the driver, which is available to any driver owning a smartphone. Method: Drowsiness and distraction alerts emitted during the journey were aggregated by continuous driving (called sub-journey). The data include 273 drivers who made 634 sub-journeys. Two binary logit models were used separately to analyze the probability of a drowsiness and distraction event occurring. Variables describing the continuous driving time (sub-journey time), the journey time (a set of sub-journeys), the number of breaks, the breaking duration time and the first sub-journey (categorical variable) were included. Additionally, categorical variables representing the gender and age of the drivers were also incorporated. Results: Despite the limitations of the retrospective data, interesting findings were obtained. The results indicate that the main risk factor of inattention is driving continuously (i.e., without stopping), but it is irrelevant whether the stop is long or short as well as the total time spent on the journey. The probability of distraction events occurring during the journey is higher than drowsiness events. Yet, the impact of increasing the driving time of the journey and stopping during the journey on the probability of drowsiness is higher than the probability of distraction. Additionally, this study reveals that the elderly are more prone to drowsiness. The data also include a group of drivers, who did not provide information on gender and age, who were found to be associated to drowsiness and distraction risk. Conclusions: The study shows that data gathered by an app have the potential to contribute to investigating drowsiness and distraction. Practical applications: Drivers are highly recommended to frequently stop during the journey, even for a short period of time to prevent drowsiness and distraction.     

Can SVM be used for automatic EEG detection of drowsiness during car driving?

This study aims to develop an automatic method to detect drowsiness onset while driving. Support vector machines (SVM) represents a superior signal classification tool based on pattern recognition. The usefulness of SVM in identifying and differentiating electroencephalographic (EEG) changes that occur between alert and drowsy states was tested. Twenty human subjects underwent driving simulations with EEG monitoring. Alert EEG was marked by dominant beta activity, while drowsy EEG was marked by alpha dropouts. The duration of eye blinks corresponded well with alertness levels associated with fast and slow eye blinks. Samples of EEG data from both states were used to train the SVM program by using a distinguishing criterion of 4 frequency features across 4 principal frequency bands. The trained SVM program was tested on unclassified EEG data and subsequently checked for concordance with manual classification. The classification accuracy reached 99.3%. The SVM program was also able to predict the transition from alertness to drowsiness reliably in over 90% of data samples. This study shows that automatic analysis and detection of EEG changes is possible by SVM and SVM is a good candidate for developing pre-emptive automatic drowsiness detection systems for driving safety.     

Exploring differences between self-report and electrophysiological indices of drowsy driving: A usability examination of a personal brain-computer interface device

IntroductionImpaired driving has resulted in numerous accidents, fatalities, and costly damage. One particularly concerning type of impairment is driver drowsiness. Despite advancements, modern vehicle safety systems remain ineffective at keeping drowsy drivers alert and aware of their state, even temporarily. Until recently the use of user-centric brain-computer interface (BCI) devices to capture electrophysiological data relating to driver drowsiness has been limited. Method: In this study, 25 participants drove on a simulated roadway under drowsy conditions. Results: Neither subjective nor electrophysiological measures differed between individuals who showed overt signs of drowsiness (prolonged eye closure) during the drive. However, the directionality and effect size estimates provided by the BCI device suggested the practicality and feasibility of its future implementation in vehicle safety systems. Practical applications: This research highlights opportunities for future BCI device research for use to assess the state of drowsy drivers in a real-world context.     

Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue

Introduction: Fatigue is one of the most crucial factors that contribute to a decrease of the operating performance of aircraft pilots and car drivers and, as such, plays a dangerous role in transport safety. To reduce fatigue-related tragedies and to increase the quality of a healthy life, many studies have focused on exploring effective methods and psychophysiological indicators for detecting and monitoring fatigue. However, those fatigue indicators rose many discrepancies among simulator and field studies, due to the vague conceptualism of fatigue, per se, which hinders the development of fatigue monitoring devices. Method: This paper aims to give psychological insight of the existing non-invasive measures for driver and pilot fatigue by differentiating sleepiness and mental fatigue. Such a study helps to improve research results for a wide range of researchers whose interests lie in the development of in-vehicle fatigue detection devices. First, the nature of fatigue for drivers/pilots is elucidated regarding fatigue types and fatigue responses, which reshapes our understanding of the fatigue issue in the transport industry. Secondly, the widely used objective neurophysiological methods, including electroencephalography (EEG), electrooculography (EOG), and electrocardiography (ECG), physical movement-based methods, vehicle-based methods, fitness-for-duty test as well as subjective methods (self-rating scales) are introduced. On the one hand, considering the difference between mental fatigue and sleepiness effects, the links between the objective and subjective indicators and fatigue are thoroughly investigated and reviewed. On the other hand, to better determine fatigue occurrence, a new combination of measures is recommended, as a single measure is not sufficient to yield a convincing benchmark of fatigue. Finally, since video-based techniques of measuring eye metrics offer a promising and practical method for monitoring operator fatigue, the relationship between fatigue and these eye metrics, that include blink-based, pupil-based, and saccade-based features, are also discussed. To realize a pragmatic fatigue detector for operators in the future, this paper concludes with a discussion on the future directions in terms of methodology of conducting operator fatigue research and fatigue analysis by using eye-related parameters.     

The detection of drowsiness using a driver monitoring system

Abstract

Objective: Drowsiness is a major cause of driver impairment leading to crashes and fatalities. Research has established the ability to detect drowsiness with various kinds of sensors. We studied drowsy driving in a high-fidelity driving simulator and evaluated the ability of an automotive production-ready driver monitoring system (DMS) to detect drowsy driving. Additionally, this feature was compared to and combined with signals from vehicle-based sensors.

Methods: The National Advanced Driving Simulator was used to expose drivers to long, monotonous drives. Twenty participants drove for about 4?h in the simulator between 10 p.m. and 2 a.m. They were allowed to use cruise control and traffic was sparse and semirandom, with both slower- and faster-moving vehicles. Observational ratings of drowsiness (ORDs) were used as the ground truth for drowsiness, and several dependent measures were calculated from vehicle and DMS signals. Drowsiness classification models were created that used only vehicle signals, only driver monitoring signals, and a combination of the 2 sources.

Results: The model that used DMS signals performed better than the one that used only vehicle signals; however, the combination of the two performed the best. The models were effective at discriminating low levels of drowsiness from moderate to severe drowsiness; however, they were not effective at telling the difference between moderate and severe levels. A binary model that lumped drowsiness into 2 classes had an area under the receiver operating characteristic (ROC) curve of 0.897.

Conclusions: Blinks and saccades have been shown to be predictive of microsleeps; however, it may be that detection of microsleeps and lane departures occurs too late. Therefore, it is encouraging that the model was able to distinguish mild from moderate drowsy driving. The use of automation may make vehicle-based signals useless for characterizing driver states, providing further motivation for a DMS. Future improvements in impairment detection systems may be expected through a combination of improved hardware, physiological measures from unobtrusive sensors and wearables, and the intelligent integration of environmental variables like time of day and time on task.     

Task-induced fatigue and collisions in adult drivers with attention deficit hyperactivity disorder

OBJECTIVE: This study compares collision involvement between adult drivers with attention deficit hyperactivity disorder (ADHD) and control participants in a simulation experiment designed to enhance the effects of fatigue. Because the effects of ADHD include difficulties in maintaining attention, drivers with ADHD were hypothesized to be more susceptible to the effects of fatigue while driving. METHODS: Data are drawn from a validated driving simulation study, portions of which were focused on enhancing the effects of fatigue. The simulator data are supplemented with written questionnaire data. Drivers with ADHD were compared with controls. RESULTS: The self-report data indicated that drivers with ADHD were more likely to report having been involved in an accident within the previous five years. Simulation data showed that time of day of participation in the experiment were significantly related to likelihood of collision, and that these effects were further exacerbated by ADHD status. Participants with ADHD were more likely than controls to be involved in a crash in the simulator regardless of time of day, but the effects were particularly pronounced in the morning, and the rate of increase in accident involvement from the late afternoon into the evening was greater among participants with ADHD. No differences in self-reported sleep patterns or caffeine use were found between participants with ADHD and controls. CONCLUSIONS: The results suggest that drivers with ADHD became fatigued more quickly than controls. Such drivers thus face greater risk of involvement in accidents on highways or open roadways where the visual and task monotony of the environment contribute to greater driver fatigue.     

基于驾驶员模型参数辨识的疲劳驾驶研究

提出一种利用驾驶员模型反演方法来进行驾驶员疲劳诊断研究的新方法。首先利用预瞄神经网络建立适应于复杂路况条件下的驾驶员-汽车-道路闭环模型,然后定义特定行驶轨迹内理论数据与试验数据的近似度为目标函数,将驾驶员参数的反演问题转化为多目标优化问题,采用基于实数编码混沌变异量子遗传算法的优化方法,获得全局最优解。试验中采用脑电和主观疲劳心理评测结合的方法确定被试者的疲劳状况。在每种疲劳状况下对驾驶员参数进行辨识,对结果进行统计分析表明,在考虑到车型、道路曲率等因素条件下驾驶员参数分布与驾驶员的疲劳状况有很强的相关性。     

Using naturalistic driving study data to investigate the impact of driver distraction on driver's brake reaction time in freeway rear-end events in car-following situation

IntroductionThe rear-end crash is one of the most common freeway crash types, and driver distraction is often cited as a leading cause of rear-end crashes. Previous research indicates that driver distraction could have negative effects on driving performance, but the specific association between driver distraction and crash risk is still not fully revealed. This study sought to understand the mechanism by which driver distraction, defined as secondary task distraction, could influence crash risk, as indicated by a driver's reaction time, in freeway car-following situations.MethodA statistical analysis, exploring the causal model structure regarding drivers’ distraction impacts on reaction times, was conducted. Distraction duration, distraction scenario, and secondary task type were chosen as distraction-related factors. Besides, exogenous factors including weather, visual obstruction, lighting condition, traffic density, and intersection presence and endogenous factors including driver age and gender were considered.ResultsThere was an association between driver distraction and reaction time in the sample freeway rear-end events from SHRP 2 NDS database. Distraction duration, the distracted status when a leader braked, and secondary task type were related to reaction time, while all other factors showed no significant effect on reaction time.ConclusionsThe analysis showed that driver distraction duration is the primary direct cause of the increase in reaction time, with other factors having indirect effects mediated by distraction duration. Longer distraction duration, the distracted status when a leader braked, and engaging in auditory-visual-manual secondary task tended to result in longer reaction times.Practical applicationsGiven drivers will be distracted occasionally, countermeasures which shorten distraction duration or avoid distraction presence while a leader vehicle brakes are worth considering. This study helps better understand the mechanism of freeway rear-end events in car-following situations, and provides a methodology that can be adopted to study the association between driver behavior and driving features.