为探索有条件自动驾驶对非驾驶相关任务的允准边界,基于实车驾驶模拟器,设计自动驾驶接管试验典型场景,招募30名被试者开展驾驶模拟试验;要求驾驶人执行3种分心形式的驾驶次任务,系统发出接管请求提示后,驾驶人接管车辆控制权以避免险情发生,并分析驾驶人接管反应时间、驾驶负荷以及驾驶绩效等相关数据.结果表明:驾驶次任务涉及的分心形式越复杂,接管过程安全性越差,视觉分心任务与操作分心任务对接管行为影响显著;驾驶人更倾向于选择制动操作接管车辆,次任务分心程度越高,制动接管比例越大;与乘客聊天对接管行为影响不显著,看视频和玩手机游戏均会显著延长接管反应时间,增加工作负荷与车辆纵向减速度,玩手机游戏还会显著提升车辆横向加速度.

为了评价公众交通安全意识,进而明确不同年龄、不同受教育程度、不同职业和不同出行方式的公众群体交通安全意识的差异性,对公众交通安全意识调查数据进行了量化分析.首先,基于自主编制的公众交通安全意识问卷对5 029名参与者进行了调查;其次,对回收的问卷进行因子分析和信度、效度检验,得到了交通安全行为、交通安全态度和交通管理认知3个因子;最后,选取上述因子作为3个一级评价指标,因子所含条目作为17个二级评价指标,对公众交通安全意识进行评价,采用主成分分析法计算因子得分,并应用熵值法确定一级评价指标权重,再通过灰色关联分析计算灰色关联度,最终根据模糊综合评价得出不同公众群体的交通安全意识水平.研究结果表明:不同年龄段的公众中,18～40岁群体交通安全意识水平最高(b=0.79),41～65岁(b=0.44)和65岁以上(b=0.45)群体交通安全意识水平较低;不同受教育程度的公众中,专科学历群体的交通安全意识水平最高(b=1),小学及以下学历群体其交通安全意识水平较低(b=0.33);不同职业的公众中,事业单位人员群体的交通安全意识水平最高(b=0.93),农民群体的交通安全意识水平较低(b=0.33);不同出行方式的公众中,以公交车为主要出行方式的群体其交通安全意识水平最高(b=0.91),而以三轮车(b=0.35)和自行车(b=0.36)为主要出行方式的群体其交通安全意识水平较低.研究结果可用于不同公众群体交通安全意识的度量,并为不同群体的交通安全意识提升方法制定提供理论依据.

为了探究车载智能终端使用对于行车安全的影响,本文概述了近年来国内外相关研究现状,提出驾驶分心的定义及类型,将可能导致驾驶人分心的车载智能终端分为手机、导航设备、收音机与CD播放机四类,分析了各类车载智能终端对行车安全的影响,发现行车时使用车载智能终端的事故几率是正常情况的4-23倍,且与操作内容相关,其中视觉分心与操作分心极易发生事故,阐述了国内外相关驾驶分心法规政策及其警示产品,概括了驾驶分心常用研究方法和评价指标,呼吁监管部门加强宣教与执法,制订车载智能终端使用及界面设计标准,最后对驾驶分心未来研究提出展望.

为预防驾驶分心导致的交通事故,利用径向基函数(RBF)神经网络模型,研究驾驶分心识别方法.通过驾驶模拟试验,分析驾驶人分别在正常驾驶、手持接听电话和免提接听电话等3种状态下执行车辆换道操作时的驾驶行为,构建基于最小正交二乘法(OLS)的RBF神经网络驾驶分心识别模型,用于判定驾驶人是否处于分心状态.研究表明:驾驶分心对换道过程中车辆的纵向速度、横向速度、横向加速度、方向盘转角、方向盘转速和油门开度等6项驾驶绩效参数有显著影响,所构建模型的平均识别正确率达到88.7％,可准确识别驾驶人的分心状态,为分心事故预防提供理论支撑.

To explore the influence mechanism of the use of in-vehicle intelligent terminals on distracted driving, this paper collected the pertinent literature of recent years and reviewed the research status according to different types of intelligent terminals. Firstly, the definition of driving distraction is generalized, and in-vehicle intelligent terminals, which may distract drivers’ attention, are divided into cellular phone, navigation devices, radio, and CD player. Correlational research was then introduced showing the influence of the use of in-vehicle intelligent terminals on drivers’ distraction. Results show that compared with general situations, using intelligent terminals when driving would increase the probability of a traffic accident from several times to dozens of times. This paper analyzed the deficiency of current researches and recommends to improve the interactive form between drivers and in-vehicle intelligent terminals. The paper expounded the laws and regulations issued by governments around the world. Lastly, main methods and content of driving distraction research were summarized and proposed that regulators need to formulate relevant regulations and polices to restrict the operation mode and interactive form for in-vehicle intelligent terminals to promote driving safety.

To explore the influence of different operating types of smartphones on drivers' carfollowing behaviors, the operating types were divided into eight classes according to their function type, and usage pattern and driving simulation experiments were conducted using the driving simulator. The car-following speed, headway, time headway, lateral deviation distance, and steering wheel angle were chosen to describe the vehicle's longitudinal and lateral running status and the entropy of distribution of the fixation point, distribution ratio of the fixation point, fixation duration, saccade frequency, saccade duration, blink frequency, and frequency duration were defined to describe the driver's vehicle operation characteristics. In addition, the driver's visual characteristics were analyzed, and ANOVA was used to verify the validity regarding whether such indicators can measure a driver's car-following behavior. The results show that specific smartphone operations could have a significant influence on the vehicle's running indicators and the driver's visual indicators. A grey relational analysis(GRA)was applied to quantify the influence of these eight operating types of smartphone on the driver's car-following behavior, and a comparison of the operating types with the same function related to the content and type of distraction, and the action time of the operation, was conducted. The results show that, when driving, the influence of these eight typical distracting smartphone operations on the driving performance are in the following order from big to small:replying to text messages, reading text messages, hand-held answering, replying to voice messages, touchtone dialing, reading voice messages, voice dialing, and hand-free answering in which the typing and reading of text messages are of greater influence than other smartphone operations. The results of this study can help drivers make clear the hazards of different operating types of smartphones on driving safety.

In order to study the driving behavior of drivers in rear-end accidents, field experiments were carried out in a certain section of urban expressway, in which near-crash events were observed.First of all,the maximum deceleration, braking to maximum deceleration time, average deceleration, and TTCi were measured for the 21 drivers.Then Mobileye and other equipment were used to extract data, on effects of factors, such as gender, driving experience and driving style, on driving behavior of drivers.Variance analyses were carried out for the data.The results show that average deceleration and the maximum deceleration of the female drivers in Near-crash events are greater than those of the male drivers, female drivers are more likely to slam on the brakes, that the experience affects the driver's average deceleration,the maximum deceleration, that the skilled driver's brake to the maximum deceleration time is longer, and the braking process is more stable, and that the driver time headway (THW) of drivers having a ratical style of driving is smaller than that of the conservative drivers.

Studies on drivers' acceptance of Advanced Driver Assistance Systems (ADAS) and its influencing factors is beneficial to promote and improve it.A total of 46 subjects are recruited in a case study to drive vehicles with and without ADAS,on 105 km typical roads in Wuhan,then complete a questionnaire of basic information and acceptance of ADAS.Based on a technology acceptance model (TAM),drivers' acceptance of ADAS is analyzed.A variance analysis method is used to study influencing factors.The results show that the average acceptance of ADAS about 43 drivers is 80.9 ％ (SD =0.191).Factors as gender,age,and driving experience of drivers have no significant influence on the acceptance of ADAS.However,different ADAS has different acceptance:FCW system is higher accepted,while LDW system is lower accepted.Different types of roads also significantly affect the acceptance,which is lowest on the city roads.