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。     

Physiological signal analysis for fatigue level of experienced and inexperienced drivers

Objective: We studied the changes in driving fatigue levels of experienced and inexperienced drivers at 3 periods of the day: 9:00 a.m.–12:00 p.m., 12:00 p.m.–2:00 p.m., and 4:00 p.m.–6:00 p.m.

Methods: Thirty drivers were involved in 120-min real-car driving, and sleepiness ratings (Stanford Sleepiness Scale, SSS; Hoddes et al. 1973 Hoddes E, Zarcone V, Smythe H, Phillips R, Dement WC. Quantification of sleepiness: a new approach. Psychophysiology. 1973;10:431–436.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]), electroencephalogram (EEG) signals, and heart rates (HRs) were recorded. Together with principal component analysis, the relationship between EEG signals and HR was explored and used to determine a comprehensive indicator of driving fatigue. Then the comprehensive indicator was assessed via paired t test.

Results: Experienced and inexperienced drivers behaved significantly differently in terms of subjective fatigue during preliminary trials. At the beginning of trials and after termination, subjective fatigue level was aggravated with prolonged continuous driving. Moreover, we discussed the changing rules of EEG signals and HR and found that with prolonged time, the ratios of δ and β waves significantly declined, whereas that of the θ wave significantly rose. The ratio of (α + θ)/β significantly rose both before trials and after termination, but HR dropped significantly. However, one-factor analysis of variance shows that driving experience significantly affects the θ wave, (α + θ)/β ratio, and HR.

Conclusions: We found that in a monotonous road environment, fatigue symptoms occurred in inexperienced drivers and experienced drivers after about 60 and 80 min of continuous driving, respectively. Therefore, as for drivers with different experiences, restriction on continuous driving time would avoid fatigued driving and thereby eliminate traffic accidents. We find that the comprehensive indicator changes significantly with fatigue level. The integration of different indicators improves the recognition accuracy of different driving fatigue levels.     

基于EEG频谱特征的驾驶员疲劳监测研究

研究表明疲劳驾驶是引发交通伤亡事故的重要原因之一,因此有必要采取相应的预防措施。脑电是公认的睡眠(疲劳)金指标,因此论文提出了基于脑电频谱特征的驾驶员疲劳预测方法。采用了驾驶模拟实验中记录的三路驾驶员脑电信号,并利用驾驶员自评与专家评定两种方式相结合的方法将驾驶数据分为疲劳和清醒。针对脑电中眼电噪声很强的特点,对记录的脑电进行了自适应滤波消噪处理,结果显示可有效滤除眼电伪迹;然后根据脑电的频域特征比较突出且与疲劳相关的特点,从去噪后的脑电中提取出了的75个频谱特征;最后利用这些频谱特征,采用朴素贝叶斯分类的方法建立了驾驶员疲劳监测模型。实验结果表明,该方法能监测出驾驶员84%的疲劳状态。     

Body Mass Index (BMI) as a first-line screening criterion for detection of excessive daytime sleepiness among professional drivers

OBJECTIVES: Obstructive sleep apnea (OSA) is the main predisposing factor of excessive daytime sleepiness (EDS), and, therefore, increases the risk of road crashes. However, it is difficult to rely on medical intake for OSA or fatigue since drivers' symptoms reports are not reliable. On the other hand, direct measurement of EDS among large numbers of drivers carries serious practical drawbacks. Obstructive sleep apnea, in turn, is strongly related to obesity, and elevated body mass index (BMI) is considered one of the major risk factors for OSA. Thus, it could be postulated that BMI may carry predictive value for EDS proneness. METHODS: The present study examined the interrelation between BMI, degree of OSA, as measured with Respiratory Distress Index (RDI), and the degree of EDS, as measured with the mean sleep onset latency in the Multiple Sleep Latency Test (MSLT) among obese (BMI = 32) professional drivers. The drivers went through polysomnography followed by the five sessions of MSLT in the next day. RESULTS: In accordance with prior studies, we have found strong correlation between BMI and the degree of OSA on the one hand, and between the degree of OSA and EDS on the other hand. OSA was detected among 77.7% of the drivers, 47.1% were sleepy, 19% had severe sleepiness [mean sleep latency [MSL < or = 5 min.], 28.1% had moderate sleepiness [MSL < or = 10 min.]. None of the drivers complained about any sleep problem, including snoring, and all reported that they do not experience excessive daytime sleepiness. Thus, there was no correlation between their subjective report and objective findings. CONCLUSIONS: Obese drivers with BMI above 32 are highly prone to be sleepy during the day. Their subjective reports of OSA or fatigue symptoms are not reliable. Therefore it is highly recommended to screen them easily by weight and height measure for further sleep study and decision about their driving abilities.     

A novel auditory working-memory vigilance task for mental fatigue assessment

An auditory working-memory vigilance task (AWVT), which involves higher mental abilities of a person, like working-memory and decision-making, in addition to vigilance, is presented for measuring human mental fatigue in this paper. A 25-h sleep deprivation study, with hourly testing by AWVT (3 min), PalmPVT (5 min) and self-report of sleepiness, is conducted on eight healthy subjects. The trend of mental fatigue level as measured by a specially proposed score, AWVT Fatigue Index (AFI), during the 25-h study shows very similar pattern to those of PalmPVT lapses and self-report sleepiness. AFI even shows closer correspondence to self-report sleepiness than PalmPVT lapses. This suggests that AWVT is able to measure performance decrement due to sleep deprivation, and it can even provide better measurement of mental fatigue than PalmPVT. AWVT shows a learning curve of less than 3 trials indicating that no skill is required in performing AWVT. Furthermore, repeat studies are done on five of the eight subjects. Pearson’s correlation analysis and other statistical exhibits suggest that AWVT has good test–retest reliability and within subject consistency, which are even better than those of PalmPVT. These results suggest that the AWVT can be used as a reliable objective measure of mental fatigue, and it can even track mental fatigue more accurately than PalmPVT in the real world where most tasks require not just a reaction time type response, but also higher mental abilities.     

基于生物数学模型的飞行疲劳预测建模与仿真

为了合理调整机组排班计划来减少飞行疲劳隐患,重点分析与排班相关的时间因素对飞行疲劳的影响,将飞行员在一天中的飞行疲劳分解为执勤期和休息期的两个分量,然后基于生物数学模型对其进行量化,建立在机组排班过程中预测飞行员飞行疲劳的数学模型。以东航A330机队部分飞行员的任务计划为例进行仿真分析,结果表明,在目标时间段内延长飞行员的连续执勤时间、增加任务总数以及缩短相邻任务间的休息时间,均会使飞行员的疲劳值增加。因此,为了降低飞行疲劳风险,机组在制定排班计划时,应适当减少飞行员的连续执勤时间,酌情增加相邻任务间的休息时间。     

The relationship between cell phone use and management of driver fatigue: It's complicated

Introduction: Voice communication may enhance performance during monotonous, potentially fatiguing driving conditions (Atchley & Chan, 2011); however, it is unclear whether safety benefits of conversation are outweighed by costs. The present study tested whether personalized conversations intended to simulate hands-free cell phone conversation may counter objective and subjective fatigue effects elicited by vehicle automation. Method: A passive fatigue state (Desmond & Hancock, 2001), characterized by disengagement from the task, was induced using full vehicle automation prior to drivers resuming full control over the driving simulator. A conversation was initiated shortly after reversion to manual control. During the conversation an emergency event occurred. Results: The fatigue manipulation produced greater task disengagement and slower response to the emergency event, relative to a control condition. Conversation did not mitigate passive fatigue effects; rather, it added worry about matters unrelated to the driving task. Conversation moderately improved vehicle control, as measured by SDLP, but it failed to counter fatigue-induced slowing of braking in response to an emergency event. Finally, conversation appeared to have a hidden danger in that it reduced drivers' insights into performance impairments when in a state of passive fatigue. Conclusions: Automation induced passive fatigue, indicated by loss of task engagement; yet, simulated cell phone conversation did not counter the subjective automation-induced fatigue. Conversation also failed to counter objective loss of performance (slower braking speed) resulting from automation. Cell phone conversation in passive fatigue states may impair drivers' awareness of their performance deficits. Practical applications: Results suggest that conversation, even using a hands-free device, may not be a safe way to reduce fatigue and increase alertness during transitions from automated to manual vehicle control.     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.     

真实条件下长时间作业的驾驶安全性影响研究

为研究长时间单调驾驶对驾驶员疲劳、嗜睡、反应时间和驾驶速度的确切影响,以合作企业3系重卡为操作对象进行了一组真实驾驶条件试验。在自愿的基础上随机地从合作企业物流公司选择12名经验丰富的驾驶员作为试验的驾驶员样本。试验采用主观评分方法记录试验者的嗜睡和疲劳状况,所有数据分析基于统计学软件PASW Statistic 18.0。试验结果表明长时间单调驾驶会导致驾驶员疲劳和嗜睡,而疲劳和嗜睡在一定的程度上会导致驾驶速度的加快,却没有导致驾驶员反应时间的显著变化。在持续驾驶3 h之后驾驶员的疲劳和驾驶绩效发生了明显劣化,故从安全的角度考虑应以2~3 h为界合理安排司机的作业负荷和绩效考评。     

作业疲劳测量方法对比研究

作业疲劳是导致生产效率下降、事故发生的重要原因之一。研究作业疲劳对提高生产效率、减少事故的发生,以及保护劳动者安全和健康具有重要意义。而作业疲劳测量方法是作业疲劳研究的前提和基础。因此,本文首先对作业疲劳的分类及其导致疲劳的原因进行了简单概述,并归纳总结了目前常用的几种作业疲劳测量方法,包括：主观感觉询问表评价法、生理参数测试法、生物化学测试法、心理学测试方法以及几种方法相结合的综合测试方法,并分析了上述几种作业疲劳测量方法的测量目的、适用范围、以及各自的优缺点,且针对不同研究目的提出了作业疲劳测量方法的选择和使用原则,并探讨了几个重点作业疲劳测量方法的研究方向,以及作业疲劳测量方法的完善方法。     

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.     

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。     

A pre/post evaluation of fatigue,stress and vigilance amongst commercially licensed truck drivers performing a prolonged driving task

Purpose. The main purpose of this research study was to evaluate changes in fatigue, stress and vigilance amongst commercially licensed truck drivers involved in a prolonged driving task. The secondary purpose was to determine whether a new ergonomic seat could help reduce both physical and cognitive fatigue during a prolonged driving task. Two different truck seats were evaluated: an industrial standard seat and a new truck seat prototype. Methods. Twenty male truck drivers were recruited to attend two testing sessions, on two separate days, with each session randomized for seat design. During each session, participants performed two 10-min simulated driving tasks. Between simulated sessions, participants drove a long-haul truck for 90 min. Fatigue and stress were quantified using a series of questionnaires whereas vigilance was measured using a standardized computer test. Results. Seat interactions had a significant effect on fatigue patterns. Conclusion. The new ergonomic seat design holds potential in improving road safety and vehicle accidents due to fatigue-related accidents.     

Identification of aggressive driving from naturalistic data in car-following situations

Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology.     

基于面部特征识别的管制员疲劳监测方法研究

为进一步控制疲劳诱发的空管人为差错,提出一套基于面部特征识别的空中交通管制员疲劳实时监测方法。应用眼动仪和视频记录系统开展36 h睡眠剥夺试验,确定PERCLOS值、平均闭眼时长、哈欠频率3个疲劳判定指标的判定阈值,并给出综合疲劳程度的融合算法。应用机器视觉的理论和方法,开发功能完整的管制员疲劳状态实时监测告警原型系统。结果表明,通过对面部特征的识别和状态融合能够实现对管制员疲劳状态的实时监测。建议在此基础上进一步开发可实用的疲劳监测系统。     

基于疲劳模式识别的VDT作业工间休息机制*

为保障作业人员身心健康和作业效率,运用E-Prime软件模拟认知性VDT持续作业,通过方差分析提出作业疲劳综合评价指标体系,并使用客观绩效指标和生理指标作为输入变量,主观疲劳综合指数作为输出变量,训练BP神经网络,对作业疲劳进行模式识别;提出认知性VDT持续作业工间休息机制。结果表明:通过正确反应时间、注视时间、瞳孔直径、眨眼频率4项指标,对VDT持续作业疲劳进行模式识别的结果可信度较高。因此,基于上述4项指标提出的工间休息机制客观有效。     

Antecedents of fatigue, close calls, and crashes among commercial motor-vehicle drivers

PROBLEM: Minimizing driver fatigue among commercial motor-vehicle (CMV) drivers is a major safety issue in the United States. This study examines the effects of potentially fatigue-inducing factors inherent in truck driving work and company safety management in explaining: (a) drivers driving while fatigued, (b) the frequency of close calls due to fatigue, and (c) actual crashes among CMV drivers. METHOD: Data for this study are derived from a survey of CMV drivers in 116 trucking firms, with all data being driver-reported. The relative roles of fatigue-inducing factors and safety management practices in explaining variation in fatigue, close calls, and crashes are reported, along with the roles of fatigue in affecting close calls and crashes via hierarchical regression. RESULTS: Findings indicated that fatigue-inducing factors inherent in driving work and safety practices accounted for appreciable variation in driving fatigued (R(2) =.42) and close calls (R(2) =.35), but not crash involvement. Driving while fatigued also accounted for incremental increases in the amount of variation in close calls, after consideration of inherent factors and safety practices. IMPACT ON INDUSTRY: Findings indicate that safety practices (e.g., establishment of a strong safety culture, dispatcher scheduling practices, company assistance with fatiguing behaviors such as loading and unloading) have considerable potential to offset fatigue-inducing factors associated with truck driving work.     

Development of an algorithm for an EEG-based driver fatigue countermeasure

PROBLEM: Fatigue affects a driver's ability to proceed safely. Driver-related fatigue and/or sleepiness are a significant cause of traffic accidents, which makes this an area of great socioeconomic concern. Monitoring physiological signals while driving provides the possibility of detecting and warning of fatigue. The aim of this paper is to describe an EEG-based fatigue countermeasure algorithm and to report its reliability. METHOD: Changes in all major EEG bands during fatigue were used to develop the algorithm for detecting different levels of fatigue. RESULTS: The software was shown to be capable of detecting fatigue accurately in 10 subjects tested. The percentage of time the subjects were detected to be in a fatigue state was significantly different than the alert phase (P<.01). DISCUSSION: This is the first countermeasure software described that has shown to detect fatigue based on EEG changes in all frequency bands. Field research is required to evaluate the fatigue software in order to produce a robust and reliable fatigue countermeasure system. IMPACT ON INDUSTRY: The development of the fatigue countermeasure algorithm forms the basis of a future fatigue countermeasure device. Implementation of electronic devices for fatigue detection is crucial for reducing fatigue-related road accidents and their associated costs.     

基于DSP虹膜识别防疲劳驾驶报警系统的研究

防止疲劳驾驶以主观监测和客观检测为主,但其在可靠性、成本、检测方法上存在不足,为此,设计了一套基于DSP虹膜识别防止疲劳驾驶报警系统。借鉴国内外学者的研究,遵循实时性、准确性、简洁性及经济性的设计原则,以虹膜识别算法为依据,DSP微处理器控制技术为基础进行了开发,该报警系统可在不干扰驾驶员的情况下,识别驾驶员身份,记录驾驶时间,识别疲劳驾驶并报警。测试结果表明:系统结构简单,实现了模块化。虹膜识别模块、计时模块基本满足了准确性和实时性的要求。     

Duty hours and incidents in flight among commercial airline pilots

Introduction. Working long duty hours has often been associated with increased risk of incidents and accidents in transport industries. Despite this, information regarding the intermediate relationship between duty hours and incident risk is limited. This study aimed to test a work hours/incident model to identify the interplay of factors contributing to incidents within the aviation industry. Methods. Nine hundred and fifty-four European-registered commercial airline pilots completed a 30-item survey investigating self-report attitudes and experiences of fatigue. Path analysis was used to test the proposed model. Results. The fit indices indicated this to be a good fit model (χ²?=?11.066, df?=?5, p?=?0.05; Comparative Fit Index?=?0.991; Normed Fit Index?=?0.984; Tucker–Lewis Index?=?0.962; Root Mean Square of Approximation?=?0.036). Highly significant relationships were identified between duty hours and sleep disturbance (r?=?0.18, p?r?=?0.40, p?r?=?0.43, p?Discussion. A critical pathway from duty hours through to self-reported incidents in flight was identified. Further investigation employing both objective and subjective measures of sleep and fatigue is needed.