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。     

长时间单调模拟驾驶对疲劳的影响研究

通过模拟驾驶实验,综合评估长时间驾驶以及单调环境对驾驶员疲劳程度的影响是笔者研究的主要课题内容。借助于在模拟驾驶座舱上,4个健康样本分别参加高速公路(单调环境)和一般公路(非单调环境)的两组驾驶仿真实验,每组测试均持续两小时,一共进行10次实验。实验过程中,样本的操控数据(车速和方向盘转角)、反应时间、心电信号、主观疲劳状况等都同步记录并保存。实验结果表明长时间驾驶对操控能力、反应时间、心率、主观疲劳都有显著性影响(p<0.050),单调环境(高速公路)和非单调环境(一般公路)相比,车速方差区别显著,而尽管被试在高速公路的单调环境下驾驶后主观感觉更疲劳一些,但反应时间、心率等因素并没有显著性差异。     

长隧道韵律型视觉环境设计研究

采用心理物理试验分析公路隧道内部视觉环境对驾驶员行车安全的影响,将E-prime 2.0软件与仿真驾驶模拟器相结合,对驾驶员在隧道内长时间行车中的速度判断准确率及反应时间两个指标进行分析,提出了利用标志标线构建公路隧道内韵律型标线系统的改善措施,以改善隧道内视觉环境,并利用数理统计方法及Logistics拟合分析对设计方案进行评价。结果表明:1)公路隧道内韵律型标线系统能提升隧道内驾驶员的速度判断准确率3.33%~11.66%;2)普通公路隧道场景中,被试者反应时间与隧道内行车时间存在显著关系,公路隧道内韵律型标线系统的场景中,反应时间与隧道内的行车时间没有显著关系,能有效缓解视觉疲劳现象;3)被试者反应时间的增加同时受隧道内视觉环境与行车时间的影响。公路隧道内韵律型标线系统能有效提高驾驶员的反应时间,适用于行驶速度为80 km/h、大于1 333 m的隧道。     

城市道路下分心行为对驾驶员驾驶绩效的影响

为探究城市道路条件下常见驾驶分心行为对驾驶绩效的影响,以16名青年志愿者为被试,开展实车驾驶试验,测量车辆纵向和横向行驶参数,对比分析正常驾驶和分心驾驶状态下的驾驶绩效,并探讨驾驶经验的影响。结果表明：3种分心行为均使横向加速度标准差和纵向加速度标准差增加,而使速度均值降低,说明驾驶员通过速度和转向控制补偿分心状态下的驾驶绩效;交谈对速度均值和纵向加速度均值、阅读广告对速度标准差和横向加速度均值具有显著性影响(p<0.05);接听电话时,经验驾驶员速度标准差明显高于新手(p=0.021);接听电话和阅读广告次任务下,经验驾驶员的横向加速度均值均明显较高(p=0.003,p=0.004),说明经验丰富的驾驶员受分心行为的影响程度更低。     

生理疲劳和心理疲劳对车辆驾驶的影响对比北大核心CSCD

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。     

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。     

老年驾驶员跟驰行为及影响因素研究

为提高老龄化背景下道路交通的安全性,基于驾驶模拟试验数据,分析老年驾驶员的跟驰行为特征,并深入研究驾驶员跟驰行为的影响因素。选取车头间距、速度和反应时间作为驾驶员跟驰行为的表征指标,通过对比老年驾驶员与年轻驾驶员跟驰行为的差异,分析老年驾驶员的驾驶特征;进一步采用Pearson相关系数法研究跟驰行为与驾驶员年龄、驾驶负荷和驾驶偏好的关系。研究结果表明:相较于年轻驾驶员,老年驾驶员对前车紧急制动行为的反应时间较长,行驶速度的变化较大,且保持较大的车头间距。驾驶员的跟驰行为与年龄、驾驶负荷得分以及驾驶偏好显著相关。     

注意瞬脱效应对驾驶安全性影响的ERP试验研究

为预防驾驶员注意瞬脱效应导致的交通事故,首先,开展模拟驾驶任务试验,并在此过程中加入刺激颜色、时间间隔等因素,分别检测驾驶员的注意瞬脱反应;然后,基于试验数据,分析不同条件下驾驶员的驾驶行为指标(反应时和正确率)与事件相关电位(ERP)指标(P3波幅和潜伏期)的差异。结果表明:较红色警示牌和2、3 s的刺激时间间隔(SOA),在黄色警示牌和1 s的SOA条件下驾驶行为绩效显著降低(反应时延长,正确率降低),且P3潜伏期在顶叶区显著延长、波幅显著减小。注意瞬脱效应影响驾驶员对驾驶事件的脑内加工进程及投入的认知资源量,导致驾驶安全性降低。     

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。     

隧道彩色路面和侧墙设计对驾驶行为的影响分析

为探究不同隧道环境设计下驾驶绩效以及驾驶员的生、心理变化特性,采用3D Max软件搭建仿真试验场景,选取15名驾驶员开展不同隧道环境设计场景下的驾驶模拟试验,采集驾驶员的生、心理及车辆运行状态数据;选取具有代表性的车辆运行指标和驾驶员状态指标,构建驾驶行为趋势面模型,分析指标之间的相对敏感程度。研究结果表明：路面铺设彩色路面可提示驾驶员适当减速,其中,黄色系渐变彩色路面和纵向减速标线彩色路面的综合效果更佳,更利于安全行车;隧道内设置装饰侧墙可缓解隧道内驾驶员视觉信息单调的现象,考虑到隧道内行车安全和通行效率,建议采用蓝-白装饰侧墙。     

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.     

Fatigued driver’s driving behavior and cognitive task performance: Effects of road environments and road environment changes

This study aims to explore the effects of different road environments and their changes on driving behaviors and cognitive task performance of fatigued drivers. Twenty-four participants volunteered in a 2 (road environment) × 3 (fatigue level) within-subjects factorial design simulated driving experiment. Participants were asked to perform basic numerical calculation and distance estimation of traffic signs when driving normally, and provide answers to a questionnaire on fatigue rating. Results show that fatigued drivers faced greater attention demand, were less alert, and tended to overestimate the distance to roadside traffic signs. Fatigue caused by driving in complex road environment had the greatest negative impact on driving behavior and visual distance estimation, and the fatigue transfer effect worsened significantly but differently on both driving behavior and performance of fatigued drivers when switching from a complex to a monotonous road environment and vice versa. Notably, this study shows that fatigued drivers performed relatively better in arithmetic tasks than non-fatigued ones. In addition, when switching from a monotonous to a complex road environment, drivers’ performance in visual distance estimation and arithmetic tasks improved though their driving behavior deteriorated, revealing that the fatigue effect upon drivers might be explained to some extent by their alertness and arousal levels.     

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。     

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。     

Working conditions and sleepiness while driving among truck drivers

Objective: The objective of this study is to identify the role of working conditions as predictors of sleepiness while driving among truck drivers.

Methods: This was a cross-sectional study carried out among truck drivers who transported grains to Paranaguá Port, Paraná, Brazil. The truck drivers were interviewed and completed a self-administered questionnaire to collect data on sociodemographic and behavioral variables, working conditions, consumption of illicit psychoactive substances, and sleep patterns. Drivers were considered to be sleepy while driving if they reported a medium or high probability of napping while driving at night, during the daytime, or while stopped in traffic. The statistical analysis used logistic regression models progressively adjusted for age, behavioral variables, sleep duration, and other working conditions.

Results: In total, 670 male drivers, with a mean age of 41.9 (±11.1) years, were enrolled. The prevalence of sleepiness while driving was 31.5%. After model adjustments, the following working conditions were associated with sleepiness while driving: Distance from the last shipment of more than 1,000?km (odds ratio [OR]?=?1.54; 95% confidence interval [CI], 1.07–2.23) and a formal labor contract with a productivity-based salary (OR = 2.65; 95% CI, 1.86–3.78). Consumption of illicit psychoactive substances (OR = 1.99; 95% CI, 1.14–3.47) was also associated with sleepiness while driving.

Conclusions: Distance traveled and a formal labor contract with productivity-based earnings were the working conditions associated with sleepiness while driving, regardless of other working or behavioral characteristics, age, consumption of illicit psychoactive substances, and sleep duration.     

道路交通环境中驾驶疲劳的生成模型研究

为预防由驾驶疲劳引起的交通事故,有必要研究在道路、交通和环境的综合影响下驾驶疲劳的生成机理。基于生理、心理学中的经典理论,借鉴国内外相关的研究成果,采用理论推理的方法对驾驶疲劳生成过程中驾驶员唤醒水平的变化规律及其影响因素进行分析。在此基础上建立了驾驶疲劳的生成模型,并将模型应用于工程实际。通过驾驶员唤醒水平的变化,指出驾驶疲劳的生成时刻,及其对驾驶时间的规定和道路、景观设计的影响。该模型以唤醒水平为核心,描述驾驶疲劳生成过程中驾驶员唤醒水平的变化规律,强调道路交通环境对驾驶员唤醒水平的影响。     

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.     

Driving in mild cognitive impairment: The role of depressive symptoms

Objectives: Previous studies indicate a negative association between depression and driving fitness in the general population. Our goal was to cover a gap in the literature and to explore the link between depressive symptoms and driving behavior in individuals with mild cognitive impairment (MCI) through the use of a driving simulator experiment.

Methods: Twenty-four individuals with MCI (mean age = 67.42, SD = 7.13) and 23 cognitively healthy individuals (mean age = 65.13, SD = 7.21) were introduced in the study. A valid driving license and regular car use served as main inclusion criteria. Data collection included a neurological/neuropsychological assessment and a driving simulator evaluation. Depressive symptomatology was assessed with the Patient Health Questionnaire (PHQ-9).

Results: Significant interaction effects indicating a greater negative impact of depressive symptoms in drivers with MCI than in cognitively healthy drivers were observed in the case of various driving indexes, namely, average speed, accident risk, side bar hits, headway distance, headway distance variation, and lateral position variation. The associations between depressive symptoms and driving behavior remained significant after controlling for daytime sleepiness and cognition.

Conclusions: Depressive symptoms could be a factor explaining why certain patients with MCI present altered driving skills. Therefore, interventions for treating the depressive symptoms of individuals with MCI could prove to be beneficial regarding their driving performance.     

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.