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。     

机车双司机驾驶行为可靠性研究

在人机系统可靠性理论与机车驾驶行为链的基础上,结合双司机作业特点,构建了基于感知、判断、操作元件及模块组的单司机及双司机驾驶行为模型。基于该模型,在确定单项器官元件可靠度测试与计算方法后,运用可靠性理论给出了功能模块可靠度、单司机驾驶行为可靠度、双司机驾驶行为可靠度模型,并对信息感知差系数、正副司机主次差系数等参数进行了分析和讨论,从而构建了一套较为完善的驾驶行为可靠性测试分析方法。最后对4组16名机车司机按事故与非事故组进行分时段连续测试与可靠度计算,其结果表明,双司机驾驶行为可靠性不仅决定于单司机驾驶行为可靠性,并且正副司机间的协调性密切相关,同时与作业时间呈凹函数分布。     

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。     

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。     

人格特征对驾驶人驾驶行为的影响研究

为了解驾驶人不安全驾驶行为背后的人格特征影响因素,回顾自2000年以来国内外人格特征与驾驶行为关系的相关研究,采用系统评价和Meta分析的优先报告项目(PRISMA)标准在国内外知名数据库检索到17 688篇文献,剔除重复文献3 160篇、不相关文献14 263篇、非优秀文献198篇,最终将67篇优秀文献纳入研究,归纳并梳理文献中不同人格特征与不安全驾驶行为的关系。结果表明：大五人格的5个人格维度与驾驶行为的关系研究结果均存在不一致;感觉寻求和冲动性各自均为不安全驾驶行为的优秀预测因子,但相较之下后者的预测能力更强;驾驶愤怒对不安全驾驶行为的直接预测能力不强;无规范感、特质焦虑、利他及自恋对均不安全驾驶行为有不同程度的影响,但相关研究较少;依恋、特质情商、集体主义和异常人格特质等人格特征受关注较少,需要更多检验;中国人样本的一些研究结果与多数研究结果存在不一致,需开展跨文化研究。     

基于驾驶模拟的自动驾驶接管行为研究

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...     

驾驶行为特性与人格特征关系研究

为了开展驾驶行为特性与人格特征关系的研究,选用26名被试人员,运用自主研 发的驾驶员综合能力测试系统,利用数理统计规律,研究被试者复杂反应、速度估计与 人格特征之间的关系。结果表明:在相同的试验环境下,所有被试者在伴有蜂鸣声时复 杂反应错误次数明显多于无蜂鸣声时;男女驾驶员在复杂反应时间方面表现出显著性差 异, 而在复杂反应错误次数方面未表现出明显差异;男女驾驶员在速度估计方面未表现 出显著性差异,但部分驾驶员表现出过高或过低估计速度的倾向;复杂反应指数和速度 估计指数与驾驶员的攻击性、独立性和敢为性之间存在着较强的正相关性,与社会向性 、稳定性之间存在着较强的负相关性;男性驾驶员表现出较强的攻击性、敢为性和独立 性,而女性驾驶员表现出较强的稳定性和社会向性。自主研发的系统能有效地分析了驾 驶行为特性与人格特征之间的内在关系,可以检测出潜在的事故倾向性大的驾驶员,有 利于减少交通事故的发生。     

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。     

绿灯倒计时信号对驾驶行为的影响

为揭示绿灯倒计时信号对交叉口交通安全的影响,针对不同交通负荷与信号控制方式下的驾驶行为开展研究。选择大连市内的倒计时信号交叉口与非倒计时信号交叉口各2处,开展车辆行驶速度及不良驾驶行为调查。用数理统计方法,对比分析2类交叉口在平峰与高峰时段的车辆行驶速度特性,同时对比分析上述不同条件下闯黄灯及违规变道行为的发生数量。研究表明,绿灯倒计时信号在绿灯时间末期,会诱发一部分驾驶员加速行驶通过交叉口;绿灯倒计时信号在平峰时段对闯黄灯行为数量的影响较小,而对违规变道行为的数量有显著影响。     

汽车驾驶愤怒情绪研究现状与展望

系统分析愤怒驾驶的研究方法和研究难点,从汽车驾驶人愤怒情绪产生的情景、汽车驾驶人愤怒时的表现或行为、汽车驾驶人愤怒情绪对交通安全的影响、汽车驾驶人愤怒情绪识别4个方面介绍汽车驾驶人愤怒驾驶的研究进展,以及国外汽车驾驶人愤怒驾驶研究的方法和工具。提出研究汽车驾驶人愤怒情绪应该重点关注样本的选择和研究方法的突破,并对发展趋势进行了展望:准确识别汽车驾驶人开车中的愤怒情绪,预测愤怒下的驾驶人开车可能产生的不安全行为,如何有效地管理愤怒驾驶,消除驾驶人在行车中的愤怒情绪,避免或降低愤怒驾驶对交通安全的影响。     

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.

Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.

Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.

Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.     

A qualitative study of college students' perceptions of risky driving and social influences

Objectives: Young adults and teens are documented as the riskiest drivers on the road, and newer issues such as texting and driving are a growing concern. This study sought to determine the risk perceptions of young adults regarding various driving behaviors, their past experiences, how their social circles are structured, and how this might affect their driving. Methods: This study conducted qualitative research with 25 college undergraduate students to determine their peer and social influences regarding distracted driving. Data were analyzed and related to the health belief model and past research on social influence. Results: Though most participants felt that their behaviors were set after learning to drive, they were, in fact, quite susceptible to the influence of those in their social circles (e.g., fear of judgment and accountability) and, more broadly, to social norms. Texting and driving was the largest and most topical distracted driving issue and was also identified as very difficult to stop due to perceived barriers and the idea that intervening is rude. Participants identified low perceived susceptibility and severity (perceived threat) for a number of risky driving behaviors, including texting and driving. Conclusions: Training is needed to encourage people to intervene and speak up regarding behaviors other than drinking and driving, and cues to action and campaigns should target intervention to increase self-efficacy, as well as norms, susceptibility, and common rationalizations for risky behavior.     

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.     

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.     

The theory of planned behavior (TPB) and texting while driving behavior in college students

Objective: To examine the role of intent and other theory of planned behavior (TPB) constructs in predicting college students' willingness to text while driving (TWD).

Methods: This was a cross-sectional study. 243 male and female college students enrolled in the 2013–2014 academic year in the College of Health, Human Services & Nursing completed a survey on TWD. Inclusion criteria: All races and ethnicities, ≥18 years of age, cell phone owner, and licensed driver.

Results: Over 70% of the sample (n = 243) reported talking on a cell phone and sending and receiving text messages “at least a few times” while driving within the past week. However, only 27% reported being stopped by police. Of these, 22% reported being fined. Within the past 30 days, 26% reported reading or sending TWD and having to slam on the brakes to avoid hitting another car or pedestrian(s) as a result. In all, 47% of the variance in intention to send TWD was accounted for by the full TPB model. Intention, in turn, predicted willingness to TWD. Intention also mediated the relationship between perceived behavioral control and willingness to TWD.

Conclusion: Attitude was found to be the strongest predictor of intention. In addition, intention was found to mediate the relationship of willingness to TWD on perceived behavioral control. These findings highlight potential factors that could be targeted in behavioral change interventions seeking to prevent TWD.     

The use of navigation systems in naturalistic driving

Objective: In this study, we assessed the use of portable navigation systems in everyday driving by applying in-vehicle naturalistic driving.

Method: Experienced users of navigation systems, 7 females and 14 males, were provided with a specially equipped vehicle for approximately 1 month. Their trips were recorded using 4 cameras, Global Positioning System (GPS) data, and other sensor data. The drivers’ navigation system use data were coded from the video recordings, which showed how often and for how long the system was activated and how often and for how long a driver operated the system.

Results: The system was activated for 23% of trips, predominantly on longer and unique trips. Analyses of the percentage of time for which the speed limit was exceeded showed no evidence of differences between trips for which the navigation system was used or not used. On trips for which the navigation system was activated, participants spent about 5% of trip time interacting with the device. About 40% of interacting behavior took place in the first 10% of the trip time, and about 35% took place while the car was standing still or moving at a very low speed; that is, 0–10 km/h.

Conclusion: These results shed light on how and when drivers use navigation systems. They suggest that although drivers regulate their use of such systems to some extent, they often perform risky tasks while driving.