使用车载信息装置对驾驶行为的影响研究

为研究使用车载信息装置对驾驶行为的影响,基于虚拟驾驶仿真平台,开展正常驾驶、操作按键式和触摸屏式收音机驾驶分心试验;应用外周视觉检测任务法(PDT)测量驾驶人反应时间、PDT目标命中率以及车速和跟驰距离等参数,以评估分心行为的心理资源需求及其对驾驶绩效的影响。试验结果表明:使用车载信息装置会降低驾驶人反应能力和驾驶绩效,操作触摸屏分心与操作按键分心相比,驾驶人反应时间延长了167 ms,PDT目标命中率降低了10%,速度保持和车距保持能力下降,使用触摸屏式车载信息装置需要更多的心理资源,对两侧视野内的信息刺激反应明显迟钝,对行车安全有较大影响。     

人机共驾环境下驾驶疲劳研究综述

为减少自动驾驶过程中驾驶疲劳对驾驶人状态的影响，综合分析人机共驾环境下驾驶人的疲劳研究发展现状，系统梳理人机共驾模式下驾驶疲劳的研究成果，探索未来发展方向。首先，通过文献检索与关联性分析，明确人机共驾过程中疲劳累积研究现状；然后，从手动驾驶和人机共驾下的驾驶疲劳致因分析、驾驶时长和非驾驶相关任务对疲劳的影响、人机共驾环境下驾驶疲劳对驾驶行为的影响3个维度，讨论分析研究成果；最后，提出人机共驾环境下驾驶人疲劳研究的不足与发展方向。研究结果表明：人机共驾模式导致驾驶人被动疲劳增加，接管绩效受损，弹性设置非驾驶相关任务与自动驾驶时间可有效缓解被动疲劳；人机共驾过程中驾驶疲劳的演化规律与检测模型尚不明确，结合人机共驾场景特征探索驾驶人疲劳调控策略是未来研究重点。     

认知分心时驾驶人应激反应特征分析

为预防由认知分心影响驾驶人应激反应能力引发的交通事故,开展试验,在驾驶模拟器平台上模拟城市、乡镇、山区和高速公路等应激场景,测试有无分心任务2种条件下驾驶人的应激反应,采集驾驶人操作数据和车辆行驶数据,并利用Facelab5型眼动仪记录驾驶人眼动数据;选取相关表征指标并分析试验数据。结果表明:相比于正常驾驶,认知分心驾驶导致驾驶人视觉搜索范围变窄;无论正常驾驶还是认知分心驾驶,扫视行为均以中、低幅度为主;认知分心导致驾驶人对加速踏板的控制能力减弱;与正常驾驶相比,认知分心时驾驶人的应激反应时间明显增加。     

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。     

手机导航方式对驾驶行为的影响研究

为研究驾驶员驾驶时使用不同手机导航方式对驾驶行为的影响,开展模拟驾驶试验,利用眼动仪获取4个场景下车辆行驶状态和驾驶人视觉参数;通过均值比较,方差和显著性分析,探究不同手机导航方式下驾驶行为存在的差异。结果表明:不同手机导航方式均造成驾驶分心,但分心程度不同;手持手机导航使驾驶人对前方和左侧区域的关注下降最为显著;使用导航时,驾驶人一般通过降低车速来减少分心带来的潜在风险,其中手持手机导航降低幅度最大;使用手机导航时,车辆纵向速度标准差更加集中,说明此时驾驶人对于车辆的控制变强。     

驾驶分心对隧道段行车安全影响分析

为研究驾驶分心对隧道段行车安全的影响,在虚拟驾驶环境下设计次任务试验,要求被试驾驶人执行多组不同类型、难度的手动与无手动次任务,同时利用眼动追踪装置采集驾驶人视觉特征参数。在筛选有效数据的基础上,运用统计学与数据挖掘方法比较驾驶人在隧道内执行不同次任务时,车辆运行速度离散性、视觉搜索区域面积、瞳孔面积变化率和次任务持续时间的差异,并分析统计显著性。结果表明,驾驶人在执行手动次任务、无手动次任务、无次任务3种状态下,执行手动次任务时车辆运行速度离散性最大、视觉搜索区域面积最小、瞳孔面积变化率最大,次任务持续时间最长;无次任务时车辆运行速度离散性最小、视觉搜索区域面积最大、瞳孔面积变化率最小;无手动次任务的试验结果居于两者之间,次任务持续时间最短。上述差异具有统计学显著性,主观感知评价与客观数据具有一致性。研究表明,手动与无手动次任务使驾驶人心理负荷明显增大,手动次任务对驾驶人的影响最为明显,因此驾驶分心次任务对隧道段行车安全影响较大。     

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。     

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。     

驾驶中使用手机对驾驶员行为安全绩效影响综述

驾驶中使用手机与交通事故之间存在着高度相关性。为揭示使用手机对驾驶行为安全绩效的影响,探索影响驾驶安全的理论机制,采取更有效的干预措施,结合近10 a来相关研究,综述了与驾驶安全密切相关的驾驶分心问题,主要包括:驾驶员分心的定义及其分类;使用手机对驾驶行为安全绩效的影响,如反应时(RT)、行车速度、路线保持和跟车距离;手机使用对驾驶员分心影响的理论机制,如信息加工理论和计划行为理论(TPB)。分析表明,使用手机会导致驾驶员的反应时延长15%～40%,驾驶路线发生明显偏移,对于行车速度减缓和跟车距离延长的假设需结合驾驶员主客观数据进行比较做进一步验证;驾驶过程中使用手机会增加驾驶员的认知负荷,TPB能够对使用手机行为进行有效的解释和预测,但对该理论中基于信念测量的研究还很少;除手机操作任务,影响驾驶员分心的其他操作任务还需做进一步的研究。     

驾驶人视觉注意力需求与驾驶行为特性的关系

采用驾驶模拟系统平台,以不同车道类型及不同交通流状态(自由流/稳定流/不稳定流/强制流)为虚拟试验场,应用心理试验设计方法,测试得到21名被试在不同交通流状态下的视觉注意力需求及驾驶行为。对5种车道类型及4种交通流状态下被试驾驶时的视觉注意力需求特性和驾驶行为特性数据进行分析,得到不同车道不同交通流状态下的视觉注意力需求变化情况。利用多元回归方法分析了不同交通流状态下驾驶人视觉注意力需求与驾驶行为之间的关系,并构建了基于驾驶行为特性的驾驶人视觉注意力需求预测模型。结果表明,驾驶人视觉注意力需求与制动次数、油门踏板位移和车辆轨迹偏差之间存在相关关系。     

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。     

智能手机应用程序使用与操作的驾驶分心风险影响研究

为研究智能手机应用程序操作方式与使用行为对驾驶分心影响的问题,探讨智能手机程序操作方式对驾驶分心影响的优劣关系,基于结构方程模型提出4个因果关系假设,构建涵盖程序使用、驾驶分心、驾驶绩效等潜变量的智能手机使用行为影响的结构方程模型。通过收集线下问卷的方式进行调查,搭建模拟场景,设计6项实验方案,通过驾驶模拟实验方式收集相关数据,建立相对偏差矩阵。研究结果表明：手机通话功能的使用(路径系数0.472)对驾驶分心影响显著,手机导航功能使用(路径系数0.256)、手机音乐功能使用(路径系数0.249)对驾驶分心影响不显著。语音交互方式均优于手动操作方式,其中语音交互启动导航方式(F₃=0.019)的影响最小。研究结果可对道路驾驶情况下智能手机应用使用与操作方式的研究起推动作用。     

Age-related differences in effects of non-driving related tasks on takeover performance in automated driving

Introduction: During SAE level 3 automated driving, the driver’s role changes from active driver to fallback-ready driver. Drowsiness is one of the factors that may degrade driver’s takeover performance. This study aimed to investigate effects of non-driving related tasks (NDRTs) to counter driver’s drowsiness with a Level 3 system activated and to improve successive takeover performance in a critical situation. A special focus was placed on age-related differences in the effects. Method: Participants of three age groups (younger, middle-aged, older) drove the Level 3 system implemented in a high-fidelity motion-based driving simulator for about 30 min under three experiment conditions: without NDRT, while watching a video clip, and while switching between watching a video clip and playing a game. The Karolinska Sleepiness Scale and eyeblink duration measured driver drowsiness. At the end of the drive, the drivers had to take over control of the vehicle and manually change the lane to avoid a collision. Reaction time and steering angle variability were measured to evaluate the two aspects of driving performance. Results: For younger drivers, both single and multiple NDRT engagements countered the development of driver drowsiness during automated driving, and their takeover performance was equivalent to or better than their performance without NDRT engagement. For older drivers, NDRT engagement did not affect the development of drowsiness but degraded takeover performance especially under the multiple NDRT engagement condition. The results for middle-aged drivers fell at an intermediate level between those for younger and older drivers. Practical Applications: The present findings do not support general recommendations of NDRT engagement to counter drowsiness during automated driving. This study is especially relevant to the automotive industry’s search for options that will ensure the safest interfaces between human drivers and automation systems.     

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...     

Effects of time-gap settings of adaptive cruise control (ACC) on driving performance and subjective acceptance in a bus driving simulator

A study was conducted to investigate the effects of time-gap settings and contents of secondary tasks on a fix-based bus driving simulator on drivers’ performance while reclaiming control from ACC in a car-following scenario of emergency brake by the lead vehicle. Thirty professional bus drivers drove on the simulator with the scenario of highway traffic flow under 12 random time-gap settings: from 0.64 s to 2.40 s with the interval of 0.16 s. As for the effects of secondary tasks, subjects were evenly divided into three conditions: no secondary task interference, simple secondary task, and complex task. The results demonstrated that different safety demarcations of time-gaps on subjective acceptance and driving performance can be found out. The integrated demarcations separated time-gaps into divisions that represented different levels of danger. It revealed that the safer time-gaps for different situations were: longer than 1.60 s for none-secondary task distraction and longer than 2.08 s for being continuously distracted by secondary tasks. The demand for simple tasks is relatively high, so a larger time-gap is needed for the driver to remain safe. This research has implications for the time-gap selection of ACC and effects of secondary task distraction on buses. A next logical step will focus on determining time-gaps for lead vehicles on curves or slopes, when multiple vehicles are present ahead, and modeling driver behavior and performance with ACC for cars, buses, and other types of vehicles.     

Identification of aggressive driving from naturalistic data in car-following situations

Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology.     

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.     

Improvement of driver active interventions during automated driving by displaying trajectory pointers—A driving simulator study

Abstract

Objective: The handover of vehicle control from automated to manual operation is a critical aspect of interaction between drivers and automated driving systems (ADS). In some cases, it is possible that the ADS may fail to detect an object. In this event, the driver must be aware of the situation and resume control of the vehicle without assistance from the system. Consequently, the driver must fulfill the following 2 main roles while driving: (1) monitor the vehicle trajectory and surrounding traffic environment and (2) actively take over vehicle control if the driver identifies a potential issue along the trajectory. An effective human–machine interface (HMI) is required that enables the driver to fulfill these roles. This article proposes an HMI that constantly indicates the future position of the vehicle.

Methods: This research used the Toyota Dynamic Driving Simulator to evaluate the effect of the proposed HMI and compares the proposed HMI with an HMI that notifies the driver when the vehicle trajectory changes. A total of 48 test subjects were divided into 2 groups of 24: One group used the HMI that constantly indicated the future position of the vehicle and the other group used the HMI that provided information when the vehicle trajectory changed.

The following instructions were given to the test subjects: (1) to not hold the steering wheel and to allow the vehicle to drive itself, (2) to constantly monitor the surrounding traffic environment because the functions of the ADS are limited, and (3) to take over driving if necessary.

The driving simulator experiments were composed of an initial 10-min acclimatization period and a 10-min evaluation period. Approximately 10?min after the start of the evaluation period, a scenario occurred in which the ADS failed to detect an object on the vehicle trajectory, potentially resulting in a collision if the driver did not actively take over control and manually avoid the object.

Results: The collision avoidance rate of the HMI that constantly indicated the future position of the vehicle was higher than that of the HMI that notified the driver of trajectory changes, χ² = 6.38, P < .05. The steering wheel hands-on and steering override timings were also faster with the proposed HMI (t test; P < .05).

Conclusions: This research confirmed that constantly indicating the position of the vehicle several seconds in the future facilitates active driver intervention when an ADS is in operation.     

Teens' distracted driving behavior: Prevalence and predictors

IntroductionTeen drivers' over-involvement in crashes has been attributed to a variety of factors, including distracted driving. With the rapid development of in-vehicle systems and portable electronic devices, the burden associated with distracted driving is expected to increase. The current study identifies predictors of secondary task engagement among teenage drivers and provides basis for interventions to reduce distracted driving behavior. We described the prevalence of secondary tasks by type and driving conditions and evaluated the associations between the prevalence of secondary task engagement, driving conditions, and selected psychosocial factors.MethodsThe private vehicles of 83 newly-licensed teenage drivers were equipped with Data Acquisition Systems (DAS), which documented driving performance measures, including secondary task engagement and driving environment characteristics. Surveys administered at licensure provided psychosocial measures.ResultsOverall, teens engaged in a potentially distracting secondary task in 58% of sampled road clips. The most prevalent types of secondary tasks were interaction with a passenger, talking/singing (no passenger), external distraction, and texting/dialing the cell phone. Secondary task engagement was more prevalent among those with primary vehicle access and when driving alone. Social norms, friends' risky driving behaviors, and parental limitations were significantly associated with secondary task prevalence. In contrast, environmental attributes, including lighting and road surface conditions, were not associated with teens' engagement in secondary tasks.ConclusionsOur findings indicated that teens engaged in secondary tasks frequently and poorly regulate their driving behavior relative to environmental conditions. Practical applications: Peer and parent influences on secondary task engagement provide valuable objectives for countermeasures to reduce distracted driving among teenage drivers.     

The role of self-regulation in the context of driver distraction: A simulator study

Objective: There is considerable evidence for the negative effects of driver distraction on road safety. In many experimental studies, drivers have been primarily viewed as passive receivers of distraction. Thus, there is a lack of research on the mediating role of their self-regulatory behavior. The aim of the current study was to compare drivers' performance when engaged in a system-paced secondary task with a self-paced version of this task and how both differed from baseline driving performance without distraction.

Methods: Thirty-nine participants drove in a simulator while performing a secondary visual–manual task. One group of drivers had to work on this task in predefined situations under time pressure, whereas the other group was free to decide when to work on the secondary task (self-regulation group). Drivers' performance (e.g., lateral and longitudinal control, brake reaction times) was also compared with a baseline condition without any secondary task.

Results: For the system-paced secondary task, distraction was associated with high decrements in driving performance (especially in keeping the lateral position). No effects were found for the number of collisions, probably because of the lower driving speeds while distracted (compensatory behavior). For the self-regulation group, only small impairments in driving performance were found. Drivers engaged less in the secondary task during foreseeable demanding or critical driving situations.

Conclusions: Overall, drivers in the self-regulation group were able to anticipate the demands of different traffic situations and to adapt their engagement in the secondary task, so that only small impairments in driving performance occurred. Because in real traffic drivers are mostly free to decide when to engage in secondary tasks, it can be concluded that self-regulation should be considered in driver distraction research to ensure ecological validity.