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。     

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。     

触电了,怎么办?/电梯安全六大注意/识别报废的灭火器

一些驾驶员总是抱怨在路上行驶罚款太多；超载的驾驶员在路上总是怕被拦车检查；心理上无时不在提心吊胆地驾驶车辆。这种不健康的驾驶心理，产生一种巨大的心理压力，不利于行车安全。     

驾驶员反应特性在沙漠环境中的表现

在沙漠等特殊环境下,驾驶员的反应特性水平是衡量驾驶员驾驶适宜性的重要指标.为了探索汽车驾驶员的反应特性在特殊环境中的表现特征,本文利用复杂(选择)反应检测仪对沙漠公路行车途中汽车驾驶员进行了实地测试,测试包括反应时间和误反应次数在内的反应特性,并根据获得的有效样本检测数据进行统计整理,对受测驾驶员反应能力总体情况及与驾驶员年龄、驾龄、持续行车时间、检测时的气温等因素之间的关系进行了重点分析.研究表明,受急躁心理影响,沙漠环境下的驾驶员反应速度明显高于平均水平,而误反应次数增多,沙漠公路受测驾驶员复杂反应时间和误反应次数平均值分别为0.74 s和2.91次.关联度分析表明,沙漠公路驾驶员的年龄、驾龄、驾车时间及测试温度等因素与反应时间和错误次数均具有较强的相关性.相对于其他几项因素,驾车时间对驾驶员反应特性的影响最为明显.     

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。     

基于灰色关联度理论的沙漠地区道路强制休息处运用体系研究

从驾驶员生理、心理特性角度,阐述了沙漠地区道路交通安全的各影响因素,分析了长距离公路驾驶员出现疲劳现象时的生理、心理特性变化,运用灰色关联度分析得到"驾驶员生理、心理特性--行车时间衰退曲线",结合目标公路附属设施设置实际情况情况,提出了更具有针对性的强制休息处运用体系.最后以阿拉尔-和田沙漠公路安全畅通工程建设为实例,详细阐述强制休息处运用体系研究情况.     

基于驾驶适宜性检测的沙漠公路驾驶员生理、心理特性研究

采用驾驶适宜性检测仪器,在塔克拉玛干沙漠公路沿线进行驾驶员心理、生理特性的现场测试。结果表明,沙漠公路特殊的环境条件,使得驾驶员表现出有别于一般绿洲公路的心理和行为特性,其主要表现在:驾驶员动体视力较静体视力的平均降幅为44.75%,最高竟达到93%;速度估计和复杂反应时间均小于全国平均;复杂反应错误次数和处置判断错误次数均高于绿洲公路。灰色关联分析表明,沙漠公路驾驶员的年龄、驾龄、驾车时间以及气温与驾驶员的各项生理、心理特性指标都具有较强的相关性,关联度最高达到0.837,其中驾车时间对各项指标的影响最大。     

沙漠公路驾驶员心理、生理特性初探

针对沙漠公路交通安全现状,采用动体视力、速度估计、复杂(选择)反应、处置判断、血压脉搏检测仪等驾驶适宜性检测仪器,就长期从事沙漠公路运输活动的驾驶员群体进行了包括视觉、速度估计、反应、判断等在内的心理、生理特性的实地测试。检测结果初步表明,沙漠公路特殊的线形布设和修筑技术特征以及沿线的自然环境条件,使得驾驶员表现出有别于一般绿洲公路的驾驶心理和行为特性,其主要表现在在沙漠环境中,驾驶员的动体视力要较静体视力受到更大的影响;急躁心理影响,沙漠公路驾驶员的速度估计和复杂反应时间相应指标值均小于全国平均水平;沙漠公路驾驶员复杂反应错误次数和处置判断错误次数均高于绿洲公路。     

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。     

基于事故深度调查的城市交通事故特征分析

为探索适合我国的事故数据深度采集标准,并分析城市道路交通事故特征及致因,基于《道路交通事故深度调查信息采集表》(简称采集表),调查人员随交警赴事故现场随机详细调查87起城市道路交通事故。借鉴"Haddon Matrix"思想建立致因分析矩阵系统,分析事故的致因。发现采集表对事故地点、事故形态及原因项分类更加具体、明确,女性驾驶员的事故发生率略低于男性驾驶员,驾驶员年龄超过60岁后,发生事故的危险性显著提高,"交叉口影响区"事故50%由变更车道引起,非机动车驾驶员未戴安全头盔是造成严重伤害的重要原因。     

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。     

Role of gender in road accidents

Influence of driver sex on road accidents is assessed in this article. Accident records for 3 years and for three different income regions were analyzed. Annual distance traveled, social and economic participation, and effect of public vehicle accidents were considered. Effects of environmental factors and driver age were also included. Driver faults analysis identified possible reasons for accident differences. Analysis of accident severity was used to assess degree of harm. Statistical analysis at the 5% significance level was used to evaluate all differences. The results show that male accident rates are significantly higher. This trend is consistent through all the analyses. Accident differences are significant only in normal driving conditions. Drivers over age 50 had the lowest accident rates. Accident rate differences were caused by lack of attention and impatience among male drivers. Appropriate means of communication should alert concerned populations to these findings.     

Accident rates of drivers with bioptic telescopic lenses

The major purpose of this study was to determine the accident rate of drivers with bioptic telescopic lenses (“bioptic drivers”) in California. The bioptic driver group consisted of 229 drivers, while a randomly selected comparison sample consisted of 21,064 drivers. The 2-year total and fatal/injury accident rates of the bioptic group were normalized to the age/sex distribution of the comparison sample. Normalized accident rates for bioptic drivers were significantly greater than the corresponding rates for comparison drivers. The study also included an analysis of the driving records of bioptic drivers prior and subsequent to acquisition of telescopic lenses and an analysis of total and fatal/injury accidents for bioptic and comparison drivers with valid licenses only. It was recommended that California continue to license bioptic drivers, but with greater use of license restrictions and more stringent post-licensing 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.     

Assessing the safety criticality of driver behavior toward cyclists at intersections

Abstract

Objective: Detailed analyses of car-to-cyclist accidents show that drivers intending to turn right at T-junctions collide more often with cyclists crossing from the right side on the bicycle lane than drivers intending to turn left. This fact has led to numerous studies examining the behavior of drivers turning left and right. However, the most essential question still has not been sufficiently answered: is the behavior of drivers intending to turn right generally more safety critical than the behavior of those intending to turn left? The purpose of this article is to provide a method that allows to determine whether a driver’s behavior toward cyclists can retrospectively be assessed as critical or non-critical.

Methods: Several theoretical considerations enriched by findings of experimental studies were employed to devise a multi-measure method. This method was applied to a dataset containing real-world approaching behavior of 48 drivers turning right and left at four T-junctions with different sight obstructions. For each driver a behavior-specific criticality was defined based on both, their driving and gaze behavior. Moreover, based on the behavior-specific criticality of each driver, the required field of view to see a cyclist from the right was defined and was set into relation with the available field of view of the T-junction.

Results: The results show that only a small portion of the drivers within the dataset would have posed an actual risk to cyclists crossing from the right side. Those situations with a higher safety criticality did not only arise when drivers intended to turn right, but also left.

Conclusion: Therefore, the analysis can only provide an explanation for the higher proportion of accidents between drivers turning right and cyclists crossing from the right side in certain situations. Further research, for example analyses of exposure data regarding the frequency of turning manoeuvers at T-junctions, is needed in order to explain the higher proportion of accidents between drivers turning right and cyclists crossing from the right side.     

塔式起重机安全性研究与展望

塔式起重机作为建筑施工的重要象征与主要运输机械，在建筑业得到了广泛应用；它的工作特点是，根据建筑需要将物品在很大空间内升降和搬运，属危险作业，因此，如果在塔式起重机的设计，制造、安装、使用和维修等环节上略有疏忽，就可能造成人员伤亡和设备事故，在实际工作中，如何提高塔式起重机的本质安全，如何合理地使用和管理好塔式起重机，让塔式起重机更好地发挥作用，是建筑业亟待解决的难题之一，本简述了塔式起重机的发展历史，分析了塔式起重机的安全现状，提出了塔式起重机安全问题的对策，展望了塔式起重机安全性技术的发展趋势。     

基于非集计理论的驾驶适宜性度量模型

为量化驾驶人的驾驶适宜性,丰富对其的检测理论和方法,应用非集计理论中的多项分对数(MNL)模型构建驾驶适宜性度量模型。模型以驾驶人一定时间内事故发生次数作为选择肢,以个人固有属性、生理心理属性14项指标作为影响因素,并根据200份实际调查数据标定各影响因素参数。另外,选取60份数据验证该模型。结果显示:14项指标参数检验值均小于1.96,各参数统计学意义显著;模型判定系数为0.364 748,表明模型拟合程度较高;且该模型计算值与统计值最大绝对误差仅为3.3%,表明模型精度较高,可用于预测驾驶适宜性。     

Classification of errors contributing to rail incidents and accidents: A comparison of two human error identification techniques

Identifying the errors that frequently result in the occurrence of rail incidents and accidents can lead to the development of appropriate prevention and/or mitigation strategies. Nineteen rail safety investigation reports were reviewed and two error identification tools, the Human factors analysis and classification system (HFACS) and the Technique for the retrospective and predictive analysis of cognitive errors (TRACEr-rail version), used as the means of identifying and classifying train driver errors associated with rail accidents/incidents in Australia. We aimed to identify the similarities and differences between the techniques in their capacity to identify and classify errors and also to determine how consistently the tools are applied. The HFACS analysis indicated that slips of attention (i.e. ‘skilled based errors’) were the most common ‘unsafe acts’ committed by drivers. The TRACEr-rail analysis indicated that most ‘train driving errors’ were ‘violations’ while most ‘train stopping errors’ were ‘errors of perception’. Both tools identified the underlying factors with the largest impact on driver error to be decreased alertness and incorrect driver expectations/assumptions about upcoming information. Overall, both tools proved useful in categorising driver errors from existing investigation reports, however, each tool appeared to neglect some important and different factors associated with error occurrence. Both tools were found to possess only moderate inter-rater reliability. It is thus recommended that the tools be modified, or a new tool be developed, for complete and consistent error classification.     

基于模糊神经网络的机动车驾驶教练员职业适宜性研究

提出应用模糊神经网络系统,建构教练员职业适宜性的3个要素(即心理素质、驾驶技能和知识的表达阐述能力)的学习样本,分析"三要素"的8项特征参数指标——场依存性、速度估计能力、交通安全意识、简单反应、选择反应、跟踪能力、行车注意力、表达阐述能力等,使用K-均值法对实验样本进行初始分类,形成标准学习样本,使用该样本对所构建系统进行训练和调试。利用经调试训练后的系统,依据所测教练员的心理、心理参数对其职业适宜性进行评价。试验表明:建立的教练员职业适宜性仿真模型能取得很好的评价效果。