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。     

生理节律对北京市出租车司机驾驶疲劳的影响

为探索生理节律对出租车司机驾驶疲劳的影响,选取具有代表性的15名北京市出租车司机,使用闪光融合频率计、动作稳定器、视觉反应时测试仪3种实验仪器,分实验组和空白组对出租车司机驾驶状态分时段进行多次测试,在数据统计分析基础上对出租车司机疲劳规律进行研究.结果 表明:出租车司机驾驶疲劳主要表现为困倦、身体不适以及倦怠3个方面.8:00司机疲劳程度最低,13:00疲劳特征关键点;7:00 ～13:00疲劳主要因素是生理节律,其显著特征是困倦,而工作方式对疲劳的影响相对较小;13:00 ～ 20:00疲劳的主要因素是工作方式和生理节律,其显著特征是困倦、身体不适和倦怠.     

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。     

基于多生理信号融合的疲劳驾驶检测研究

为减少交通事故,保障道路交通安全,提出一种能更加有效地检测驾驶人驾驶状态的方法。通过对疲劳状态进行等级划分,利用脉搏波信号(Photoplethysmographic signal, PPG)以及皮肤电反应信号(Galvanic Skin Response, GSR),实现多种生理信号融合,进而构建驾驶人的驾驶疲劳状态数据库。根据采集数据结合主观评测分析驾驶人状态变化规律,选取有效指标进行分析比较,以探究各个指标与疲劳程度的变化趋势。依据状态变化规律和特征,结合主观评测,分析驾驶人的疲劳状态。同时,设定疲劳状态等级,分为清醒、轻度疲劳和重度疲劳状态,构建隐马尔可夫(Hidden Markov Model, HMM)驾驶疲劳水平分级的疲劳评估模型。测试结果显示：训练后的HMM疲劳检测模型准确率为90%。     

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。     

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。     

浅谈疲劳对安全的影响

论述了安全心理学的概念和意义,对心理学因素之一－疲劳对工作能力的影响进行了分析。提出运用心理学的方法提高工作效率,减少疲劳,保证安全。     

浅谈疲劳对安全的影响

论述了安全心理学的概念和意义,对心理学因素之一—疲劳对工作能力的影响进行了分析。提出运用心理学的方法提高工作效率,减少疲劳,保证安全。     

矿工心理疲劳试验研究

为了研究矿工心理疲劳,减少煤矿人因事故,保障煤矿安全生产,以煤矿工人为研究对象,通过双类内隐联想试验(IAT)和外显调查问卷相结合的方法对煤矿工人心理疲劳进行实证研究.首先,通过自由联想问卷、李克特量表及专家讨论确定试验材料;其次,通过卡内基梅隆大学等开发的试验生成系统E-prime 2.0软件编制内隐试验程序,试验预测矿工内隐心理疲劳;再次,借用马斯拉奇心理疲劳量表中文修订版,测量矿工的外显心理疲劳;最后,运用SPSS 25.0和Excel 19.0分析内隐和外显心理疲劳,以及内隐与外显心理疲劳的关系.结果表明:被试整体上存在心理疲劳;内隐和外显心理疲劳之间不存在相关关系或存在弱相关关系,它们属于不同的建构.     

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。     

体力疲劳对建筑工人不安全行为的影响效应研究

为降低建筑安全事故发生的概率,从建筑工人的不安全行为入手,构建体力疲劳与不安全行为理论影响模型,通过结构方程模型(structural equation model, SEM)与层次回归法实证检验二者间的影响关系。研究结果表明：体力疲劳直接正向影响建筑工人不安全行为,且通过工作倦怠作为中介因素间接影响建筑工人不安全行为;而心理资本反向调节体力疲劳与工作倦怠的关系。研究结果揭示体力疲劳与不安全行为的中介调节机制,丰富和完善二者的间接及整体关系,有助于管理者有效防控建筑工人的不安全行为。     

疲劳驾驶交通事故的严重程度影响因素分析

为探究和定量分析疲劳驾驶交通事故严重程度的影响因素,以广东省1 370条疲劳驾驶事故数据为基础,对比分析不同年份、时间段以及年龄段的疲劳驾驶交通事故特征;以交通事故严重程度为因变量,将其分为严重事故和非严重事故,从驾驶员年龄、驾龄、车辆类型等17个初步选择的自变量中筛选对疲劳驾驶交通事故严重程度具有显著影响的因素;采用二元Logistic回归模型分别对全体数据和不同道路类型下的数据建立疲劳驾驶交通事故严重程度预测模型,并对模型进行参数估计和检验。研究结果表明：模型拟合度良好,准确性高;对疲劳驾驶交通事故严重程度具有显著影响的因素有年龄、人员类型、车辆类型、道路类型、道路线形和能见度;车辆类型和道路线形是影响城市道路交通事故严重程度的重要因素,能见度是影响1,2级及其他更低级道路交通事故严重程度的重要因素。     

疲劳驾驶交通事故的特点分析与预防

疲劳驾驶引发的道路交通事故导致了严重的人身伤亡和财产损失。本文从心理和生理两方面分析了疲劳形成机理,对我国2006年因疲劳驾驶导致的交通事故进行了疲劳事故特点分析,并根据统计分析结果制定针对性的预防措施,从而减少疲劳驾驶交通事故的发生率。     

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.     

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.     

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.     

考虑驾驶方式的车辆与航空器交叉冲突评估*

为量化不同驾驶方式下车辆与航空器行驶风险,提出车辆与航空器交叉运行冲突评估模型,该模型将特种车辆驾驶方式分为激进、稳定、保守3类,综合驾驶特性和管制规则定义车辆速度演化规律,结合运行场景、间隔配备等要素确定冲突条件,并基于航空器与车辆实时速度、位置变化构造冲突评估模型。研究结果表明:冲突评估模型能够计算车辆与航空器在十型交叉口的冲突概率;激进方式下车辆先于航空器通过交叉口,风险概率均值相对最大,为0.679;保守方式下航空器先于车辆通过交叉口,最大间隔是安全间隔的4.7倍;稳定驾驶方式可兼顾安全和效率。本文模型能再现十型道口车辆与航空器交叉运行冲突产生、发展及解脱过程,计算结果可用于场面实时冲突识别和预警,能够为机场危险源识别和风险管控提供依据。     

Effects of environment, vehicle and driver characteristics on risky driving behavior at work zones

This study aims to analyze the effects of environment, vehicle and driver characteristics on the risky driving behavior at work zones. A decision tree is developed using the classification and regression tree (CART) algorithm to graphically display the relationship between the risky driving behavior and its influencing factors. This approach could avoid the inherent problems occurred in the conventional logistic regression models and further improve the model prediction accuracy. Based on the Michigan M-94/I-94/I-94BL/I-94BR highway work zone driving behavior data, the decision tree comprising 33 leaf nodes is built. Bad weather, poor road and light conditions, partial/no access control, no traffic control devices, turning left/right and driving in an old vehicle are found to be associated with the risky driving behavior at work zones. The middle-aged drivers, who are going straight ahead in their vehicles with medium service time and equipped with an airbag system, are more likely to take risky behavior at lower work zone speed limits. Further, the middle-aged male drivers engage in risky driving behavior more frequently than the middle-aged female drivers. The number of lanes exhibits opposing effects on risky behavior under different traveling conditions. More specifically, the risky driving behavior is associated with the single-lane road under bad light or weather conditions while drivers are more likely to engage in risky behavior on the multi-lane road under good light conditions.