基于生存分析的自动驾驶接管时间分析

为探究有条件自动驾驶接管过程中非驾驶相关任务、接管请求时间、接管事件类型及交通流量对接管时间的影响,利用正交试验原理设计并搭建接管场景,招募驾驶人进行自动驾驶接管试验,采集试验过程中的车辆运行状态及驾驶人行为数据,分析不同因素对驾驶人接管时间的影响,并构建随机效应生存分析模型,预测接管时间。结果表明:与无次任务相比,执行非驾驶相关任务会显著增加接管时间;接管请求时间为4 s时的接管时间要显著小于5和6 s时的接管时间;与施工区事件相比,事故车辆与换道并减速事件下驾驶人的接管时间较长;交通流量越大,接管时间越短。相比于固定效应生存分析模型,随机效应生存分析模型能揭示数据中未观测到的异质性,具有良好的拟合优度及预测效果。     

驾驶员接管自动驾驶车辆的眼动特性和行为分析

为探究接管自动驾驶车辆期间驾驶员的视觉特性,分析眼动与接管反应操控行为的关系,开展驾驶模拟试验收集驾驶行为及眼动数据。运用统计学方法,分析驾驶员感知不同接管场景的视觉特性,探究接管请求(TOR)前后眼动指标的变化规律;并基于视觉分配和瞳孔变化特性分析驾驶行为,揭示眼动特性与接管反应及驾驶操纵策略的内在联系。结果表明:TOR前,相较于静态场景,驾驶员感知动态场景诱发元素扫视更频繁且平均注视时间更短;此时驾驶员的视觉分配特性与其接管反应行为存在显著相关性。TOR后,驾驶员的注视时间增加,眨眼频率降低,瞳孔直径扩张,眼跳幅度增大;不同场景下驾驶员的瞳孔差异表明其应对动态场景时具备更好的警戒水平和更平稳的操纵策略。     

基于计划行为理论的侵犯驾驶行为研究

从社会心理学的角度,通过问卷调查的方式,对驾驶员侵犯驾驶行为的产生动机进行分析;基于计划行为理论,研究构建了能揭示驾驶态度与行为关系的理论模型,并得出驾驶员对侵犯驾驶行为的态度、主观标准、知觉行为可控性等心理因素,通过行为意向的中介作用,对驾驶员实施侵犯驾驶行为具有预测作用的结论.同时,提出了从态度改变、建立社会支持体系、提高驾驶员的自我能力评估等方面入手对驾驶员侵犯驾驶行为进行矫正的策略.     

基于换道行为的驾驶分心识别方法

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。     

驾驶行为理论模型研究概述

通过回顾驾驶行为的研究概况,重点介绍了4种理论模型:计划行为理论、威慑理论、事故原因模型、情境中介模型。在此基础上,分析了我国驾驶行为研究现状,并提出开展我国驾驶行为研究的初步建议和未来的研究方向:发展适用于我国的驾驶行为模型,并进一步研究在我国文化背景和道路环境下,不安全驾驶行为与示范性规范、文化因素以及情境变量等因素之间的关系,从心理学角度减少不安全驾驶行为和事故卷入,改善交通安全,提高交通效率。     

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.     

融合分心维度的驾驶行为可靠性研究

分心驾驶容易影响驾驶行为,进而导致交通事故的问题。用理论建模方法,研究分心对驾驶行为及其可靠性的影响。介绍分心驾驶的定义和维度。基于驾驶行为理论,建立融合分心维度的驾驶行为模型,分析不同分心维度对驾驶行为的影响机理。基于可靠性理论,建立融合分心维度的驾驶行为可靠性模型,分析不同分心维度对驾驶行为可靠性的影响。结果表明:不同维度分心对驾驶行为的影响具有各自特点和交互性;减少影响系数高的分心维度,有助于提高驾驶行为可靠性;多维度分心比单维度分心会更大程度地降低驾驶行为可靠度,应减少或避免复合分心。     

基于个性化行为模型的驾驶疲劳识别方法

为提高疲劳驾驶状态的识别精度,应考虑驾驶人之间的个体差异。以实车驾驶试验条件下车道保持行为中的车速和车道偏离值为输入,以方向盘转角为输出,基于径向基(RBF)神经网络针对每个驾驶人构建正常驾驶状态下的车道保持行为模型,并根据残差对模型的拟合及预测效果进行评价;将疲劳驾驶状态下的车速和车道偏离值输入到上述驾驶行为模型中,可得到模型预测的方向盘转角值,通过分析预测值与实际方向盘转角之间的差异,研究疲劳对驾驶人行为的影响;将预测残差作为输入,建立基于支持向量机(SVM)的疲劳驾驶状态辨识模型。结果表明:所建立的RBF神经网络-SVM识别模型对不同驾驶人疲劳驾驶状态的平均识别率达85%。     

基于实车驾驶试验的螺旋匝道小客车纵向驾驶行为研究

为明确螺旋匝道(螺旋桥)驾驶操纵行为及车辆状态特征,选取2条桥头立交螺旋匝道和2座螺旋桥,开展小客车实车路试,通过连续采集行驶速度、加速度、踏板力等驾驶数据,分析螺旋匝道(桥)及前后衔接段的纵向驾驶行为特性.结果表明:行驶速度在螺旋匝道范围内基本恒定,并明显高于设计速度,螺旋匝道段的速度分布比主线段更集中,螺旋匝道对驾...     

自动挡车驾驶注意事项

和傻瓜相机一样,自动挡车经常被人们当成是不需要任何驾驶技术的傻瓜车。其实,自动挡汽车虽然省去了离合器踏板,使驾驶变得更加简单轻松,但若操作不当,也会增加自动变速器的故障发生几率,而正确使用自动变速器,不仅可以延长变速器的使用寿命,还可以降低油耗。技术顾问给我们指出了几个自动挡车的驾驶注意事项。     

Multi-agent traffic simulations to estimate the impact of automated technologies on safety

Abstract

Objective: The objective of this article was to develop a multi-agent traffic simulation methodology to estimate the potential road safety improvements of automated vehicle technologies.

Methods: We developed a computer program that merges road infrastructure data with a large number of vehicles, drivers, and pedestrians. Human errors are induced by modeling inattention, aimless driving, insufficient safety confirmation, misjudgment, and inadequate operation. The program was applied to simulate traffic in a prescribed area in Tsukuba city. First, a 100% manual driving scenario was set to simulate traffic for a total preset vehicle travel distance. The crashes from this simulation were compared with real-world crash data from the prescribed area from 2012 to 2017. Thereafter, 4 additional scenarios of increasing levels of automation penetration (including combinations of automated emergency braking [AEB], lane departure warning [LDW], and SAE Level 4 functions) were implemented to estimate their impact on safety.

Results: Under manual driving, the system simulated a total of 859 crashes including single-car lane departure, car-to-car, and car-to-pedestrian crashes. These crashes tended to occur in locations similar to real-world crashes. The number of crashes predicted decreased to 156 cases with increasing level of automation. All of the technologies considered contributed to the decrease in crashes. Crash reductions attributable to AEB and LDW in the simulations were comparable to those reported in recent field studies. For the highest levels of automation, no assessment data were available and hence the results should be carefully treated. Further, in modeling automated functions, potentially negative aspects such as sensing failure or human overreliance were not incorporated.

Conclusions: We developed a multi-agent traffic simulation methodology to estimate the effect of different automated vehicle technologies on safety. The crash locations resulting from simulations of manual driving within a limited area in Japan were preliminary assessed by comparison with real-world crash data collected in the same area. Increasing penetration levels of AEB and LDW led to a large reduction in both the frequency and severity of rear-end crashes, followed by car-to-car head-on crashes and single-vehicle lane departure crashes. Preliminary estimations of the potential safety improvements that may be achieved with highly automated driving technologies were also obtained.     

基于隐性危险驾驶人感知特性及判别模型研究

为研究驾驶人在隐性危险场景中危险感知行为,基于Bayes判别法,将驾驶行为指标作为输入变量,建立驾驶人危险感知能力预测模型.结果表明:驾驶员在显性危险场景的纵向加速度显著低于隐性危险场景;驾驶人在隐性危险场景中TTC值小于显性危险场景;在被试车辆与轿车、行人、非机动车冲突3种交通冲突场景中,驾驶人对行人危险感知能力最弱...     

Driver’s mental workload prediction model based on physiological indices

Developing an early warning model to predict the driver’s mental workload (MWL) is critical and helpful, especially for new or less experienced drivers. The present study aims to investigate the correlation between new drivers’ MWL and their work performance, regarding the number of errors. Additionally, the group method of data handling is used to establish the driver’s MWL predictive model based on subjective rating (NASA task load index [NASA-TLX]) and six physiological indices. The results indicate that the NASA-TLX and the number of errors are positively correlated, and the predictive model shows the validity of the proposed model with an R² value of 0.745. The proposed model is expected to provide a reference value for the new drivers of their MWL by providing the physiological indices, and the driving lesson plans can be proposed to sustain an appropriate MWL as well as improve the driver’s work performance.     

基于HRV量化的高速公路出口匝道区域驾驶负荷特性研究

为提高高速公路主线出口匝道区域的行车安全性,基于HRV指标量化分析该区域的驾驶负荷特性和影响因素.分析了交通标志设计方案对主线出口匝道区域交通安全影响特性.采用HRV指标对驾驶负荷进行量化分析,同时采用不同信息量的交通标志对驾驶负荷进行定量加载,以汉十高速公路沿线匝道区为场景,设计并开展了主线出口匝道区域驾驶负荷的实车道路试验,采集不同试验场景下多个驾驶人的生理指标和驾驶特性.结果表明,交通标志信息量可以作为认知负荷加载的方式.从2 km到500m到出口位置的预告标志,表征驾驶负荷的HRV指标呈先下降后上升的趋势.驾驶人在通过主线出口匝道区域不同路段时的行驶车速的变化趋势与驾驶负荷强度的变化趋势具有较好的一致性.     

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。     

Identifying risky drivers with simulated driving

Objective: The present study aimed to examine whether high-risk drivers differ from low-risk drivers in driving behavior in a simulated environment.

Method: The 2 risk groups including 36 drivers (18 males and 18 females) performed driving tasks in a simulated environment. The simulated driving behaviors are compared between the 2 risk groups.

Results: The high-risk drivers drove much faster and exhibited larger offsets of the steering wheel than did the low-risk drivers in events without incidents. Additionally, the high-risk drivers used turn signals and horns less frequently than the low-risk drivers.

Conclusions: The present study revealed that the high-risk group differed from the low-risk group in driving behavior in a simulated environment. These results also suggest that simulated driving tasks might be useful tools for the evaluation of drivers’ potential risks.