认知过程中交通标志视认有效性影响因素分析

应用认知心理学的理论及方法,对驾驶员感知交通标志的心理过程进行分析,提出一种将驾驶员的直觉、分析和推理三者相结合的驾驶员信息处理模型。从标志和驾驶员特征出发对影响标志视认性的因素给出基于心理感知的理论分析。该分析结果为驾驶员认知过程研究、标志视认有效性的评价指标选取和相关系统开发设计提供了依据。     

LED交通标志的视认性试验研究与分析

为帮助驾驶员赢得更多反应操作时间从而提高行车安全性,研究分析交通标志的视认性,尤其是夜间视认性。以太原市中环路试设的LED主动发光指路标志及同规格的反光膜标志为研究对象,分别在昼夜条件下对其视认特性开展试验研究。研究表明,驾驶员对标志的视认性与其环境及自身条件有关;LED主动发光交通标志在白天与夜间的视认性相差甚微,但其在夜间的视认距离明显优于反光膜标志,是反光膜标志的1.3~1.9倍。     

半透发光式与逆反射式指路标志视认效果对比分析

为保证隧道内驾驶员对指路标志的安全视认,分析隧道照明光环境、标志特性对驾驶员视认距离的影响,营造了隧道中间段5种色温、3种亮度照明光环境,随机选取了20名小客车驾驶员进行半透发光式与逆反射式指路标志的视认试验。结果表明:隧道内半透发光式指路标志的视认距离明显优于逆反射式指路标志,且半透发光式指路标志的最佳字体照度范围为3 000～3 500 lx;在最佳的背景光环境条件下,半透发光式指路标志与字高53 cm,笔画在5画以下、5～10画及10画以上的逆反射式指路标志达到相同的视认效果时,字高分别缩减了35. 85%、30. 19%和24. 53%。半透发光式指路标志的应用可提高隧道内指路标志的视认距离,并能减少交通事故的发生,可为以后隧道内交通标志的改进与创新提供理论支撑。     

驾驶员自身因素对限速标志识别能力的影响分析

以研究城市道路限速标志下驾驶行为的变化为主要目的,基于认知心理学知识,采用实地观测和问卷调查等手段,研究了驾驶员经过限速标志时的心理及反应过程。依据认知工效学中的注意和记忆等机制对驾驶员的限速标志认知过程进行了解析;从驾驶员自身角度分析了影响驾驶员识别限速标志的主要因素;利用因子分析方法对调查数据和调查项目进行统计分析;最终提取了影响驾驶员识别限速标志的6个自身因素为主因子,作为限速标志对驾驶行为影响较大的因素。该研究结果可为城市道路限速标志的设计与设置提供科学依据。     

ATIS环境下驾驶员认知负荷研究进展

为构建更加符合驾驶员认知特性的出行信息环境,提高驾驶员出行信息服务水平,对ATIS环境下驾驶员认知负荷研究进展进行系统分析与评述.首先阐述了认知负荷的理论基础,随后分析了ATIS环境下驾驶员认知负荷的影响因素,同时分类解析了驾驶员内在认知负荷、外在认知负荷与相关认知负荷的产生根源,并对认知负荷的形成机理进行了分析;系统评述了驾驶员认知负荷的测量方法,最后基于驾驶员信息认知负荷提出了ATIS优化对策.结果表明:年龄、性别、受教育程度、月收入、出行经验是影响驾驶员认知负荷的主要因素;道路交通标志的版面设计与驾驶员的标志视认时间关联密切;驾驶过程中拨打手机及使用车载终端均会分散驾驶员注意力,增加驾驶员的信息认知负荷;较高的车速与音频播放等环境会对驾驶员的信息认知能力提出更高要求.     

基于事故分析的公路限高门架警告标志优化设置*

为优化设置公路限高门架警告标志,利用Kruskal-Wallis检验与二元logistic回归分析,从工效学及人因工程学角度剖析事故原因,基于大型车辆驾驶员视认特征与道路交通环境特点,提出警告标志前置设置最低要求,构建考虑大型车辆驾驶员视认角度标志前置距离模型,并提出预防措施。结果表明:限高门架高度警示标志设置不规范,导致停车安全距离不足,标志视认能力下降;根据模型得出公路限高门架警告标志前置参考距离,验证模型有效性,并有针对性提出限高门架警示标志优化设置意见。研究结果可为公路限高门架警告标志设置提供参考和借鉴。     

城市长隧道导向视觉参照系安全改善效果研究

为保障城市长隧道交通安全,提高运行效率,分析含多出口匝道的城市长隧道视觉环境特征,结合视错觉机制与色彩心理学原理构建空间导向视觉参照系;基于E-prime软件开展驾驶员心理物理学感知试验,研究该视觉参照系对城市长隧道驾驶安全的改善效果以及最优设计形式。结果表明:蓝、绿信息环境下驾驶员行车舒适度最高,黄色信息警示性强且不会造成剧烈刺激,有利于提高驾驶员注意力;该视觉参照系能够缩短驾驶员对导向信息的视认时间,增加视认距离;韵律图案向隧道进出口适度倾斜以诱导驾驶员产生坡度错觉,当倾斜75°时,诱导驾驶员在坡道主动控速的效果最佳。     

城市道路养护作业对驾驶员安全驾驶的影响

从市政养护的满意度、驾驶员行为调查以及城市交通标志识别等方面展开调查,发现私家车、公交车和出租车对道路养护作业的满意度存在显著差异,3类车型的驾驶员在安全驾驶行为上也存在明显差异。私家车驾驶员的驾驶失误行为明显高于出租车和公交车驾驶员。违规越多的驾驶员,其驾驶攻击性行为越强。同时道路养护中的常见标志对不同类型的驾驶员产生不同的信息量。私家车驾驶员对于交通标志的理解程度明显高于出租车和公交车驾驶员。     

交通标志语义认知与情绪加工的脑电研究

为了让驾驶员更正确快速地理解交通标志,避免不当驾驶行为,探究驾驶员对交通标志的语义认知和情绪加工过程。采用刺激1-刺激2(S1-S2)试验范式,基于事件相关电位(ERPs)技术和脑电信号(EEG)时频(TF)分析方法,研究交通标志语义一致性和情绪唤醒的客观评价指标。结果显示,交通标志和文字语义不一致条件下,同一时间窗表现出更大的N400和θ波活动,证实了交通标志和文字之间存在较大的语义距离;交通标志和文字语义一致条件下,诱发更大的晚期正成分(LPP);N400、θ波和LPP可以用于判断交通标志的语义距离和情绪唤醒。ERPs结合EEG的TF分析结果能从神经科学的视角解释行为数据,可用于测量交通标志理解度。     

“特色交通标志”设计及其信息量度量方法研究

为提高道路交通标志准确适时的信息传递功能,在对交通标志信息传递原理进行分析的基础上,对"特色交通标志"的设计及交通标志承载的信息量量化方法进行深入研究。基于以人为本的思想,提出便于用路者辨识的"特色交通标志"设计理念,并以青藏公路为例,设计具有西藏地域特色的人性化交通标志。将视觉选择性原理引入到传统信息传递过程中,建立特色交通标志信息传递的理论模型。结合信息论中信息度量方法,提出交通标志信息量数学量化方法,并将普通交通标志与"特色交通标志"的信息量进行对比。结果表明:"特色交通标志"信息量往往较大,但在不超过标志承载最大信息量286.3 bit的前提下,"特色交通标志"信息传递性更强,更易使人们接受。     

基于认知心理学的驾驶员信息加工模式研究

道路交通系统是一个有人参与的复杂系统,人,特别是驾驶员的行为决定了相当一部分系统的性能。而驾驶行为是一个不断往复进行的信息处理过程,对驾驶员的信息加工模式进行研究是道路交通安全与汽车安全设计的核心基础。随着认知工效研究的深入和人工智能的发展,传统的基于刺激(S)-机体(O)-反应(R)的驾驶员信息加工模式,难以满足技术发展的要求。笔者在对交通系统建模与分析的基础上,应用现代认知心理学理论,对驾驶员的信息获取和处理机制进行了研究,建立了驾驶员对某一确定认知对象的信息处理结构模型,并对模型的作用和特点进行了简单的分析。分析结果为驾驶员认知过程研究和相关系统开发设计提供了依据。     

The effects of roadway environment and fatigue on sign perception

Drivers were stopped 200 m (656 ft) after passing a warning sign and tested for their recall and recognition of the sign. An average of 5 to 10% of drivers registered the sign under various conditions, but these results were independent of specific sign content or roadway environment. Both objective and subjective measures of fatigue were related to the probability of seeing a sign on a straight and level road but not on a hilly and winding road. It was concluded that under normal daylight conditions warning signs are either redundant (contain information directly available) or irrelevant to the driver's perceived needs and the driving task. Before ruling out their usefulness, warning signs should be evaluated under conditions of degraded visibility.     

The effects of driver factors and sign design features on the comprehensibility of traffic signs

PROBLEM: This paper addresses the effects of driver factors and sign design features on the comprehensibility of traffic signs. METHODS: A survey was designed to capture subjects' personal particulars, ratings on sign features, and comprehension scores, and then administered to 109 Hong Kong full driving license holders. RESULTS: Years with driving license and education level were significant predictors of sign comprehensibility. Contrary to expectation, the driver factors of age group, years of active driving, hours of driving, last time driving, driving frequency, and non-local driving experience had no effect on comprehension performance. Sign familiarity was correlated with comprehension score for licensed drivers, whereas sign concreteness, simplicity, and meaningfulness were not. IMPACT ON INDUSTRY: The results of this study provide useful guidelines for designing more user-friendly traffic signs in the future. It identified particular driver groups who lacked good understanding of traffic signs, and this information may assist the relevant organizations to better allocate traffic training resources, and better target future studies of traffic sign comprehension.     

Improvement of the performance of animal crossing warning signs

Introduction: Animal–vehicle collisions (AVCs) can result in serious injury and death to drivers, animals' death, and significant economic costs. However, the cost effectiveness of the majority of AVC mitigation measures is a significant issue. Method: A mobile-based data collection effort was deployed to measure signs under the Utah Department of Transportation's (UDOT) jurisdiction. The crash data were obtained from the UDOT risk management database. ArcGIS was employed to link these two data sets and extract animal-related crashes and signs. An algorithm was developed to process the data and identify AVCs that occurred within sign recognition distance. Kernel density estimation (KDE) technique was applied to identify potential crash hotspots. Results: Only 2% of AVCs occurred within the recognition distance of animal crossing signs. Almost 58% of animal-related crashes took place on the Interstate and U.S. highways, wherein only 30% of animal crossing signs were installed. State routes with a higher average number of signs experienced a lower number of AVCs per mile. The differences between AVCs that occurred within versus outside of sign recognition distance were not statistically significant regarding crash severity, time of crash, weather condition, driver age, vehicle speed, and type of animal. It is more likely that drivers become accustomed to deer crossing signs than cow signs. Conclusions: Based on the historical crash data and landscape structure, with attention given to the low cost safety improvement methods, a combination of different types of AVC mitigation measures can be developed to reduce the number of animal-related crashes. After an in-depth analysis of AVC data, warning traffic signs, coupled with other low cost mitigation countermeasures can be successfully placed in areas with higher priority or in critical areas. Practical applications: The findings of this study assist transportation agencies in developing more efficient mitigation measures against AVCs.     

Investigating the self-reported behavior of drivers and their attitudes to traffic violations

IntroductionDriving behavior theoretical models consider attitudes as an important determinant of driver behavior. Moreover, the association between the self-reported tendency to commit violations and accident involvement is widely recognized. This research investigates drivers’ self-reported behavior and attitudes to risky behaviors related to the traffic violations of speeding, drink-driving, and cell phone use using cluster analysis.MethodA sample of 601 Greek drivers participating at the SARTRE 4 pan-European survey is utilized. The analysis identified three clusters of drivers. Drivers in Cluster 1 commit traffic violations more often; drivers in Cluster 2 favor traffic violation countermeasures while having moderate views toward compliance with traffic rules; and drivers in Cluster 3 strongly support traffic violation countermeasures and also have strong views toward compliance with traffic rules. Risky behaviors and related attitudes that differentiate the three distinct groups of drivers (clusters) were determined.ResultsThe findings indicate that differences in attitudes and behaviors may be attributed to factors such as age, gender, and area of residence. The research findings also provided some insight about the current level of drivers’ attitudes to traffic violations, especially those that negatively affect traffic safety. The pattern of their views on violations may form the basis of risk behavior-related interventions tailored to the identified groups, aiming at informing, educating, and raising the awareness of the public.Impact on IndustryAgencies focused on safety interventions could exploit this information in designing and implementing education campaigns, enforcement programs and in defining relevant priorities.     

Investigating the role of socio-economic factors in comprehension of traffic signs using decision tree algorithm

IntroductionDrivers' ability to comprehend the meaning of traffic signs is essential to safe driving. Drivers' personal characteristics are believed to play a crucial role in determining drivers' comprehension of traffic signs.MethodThis study investigates the role of age, gender, marital status, license category, educational level, driving experience, monthly income, and number of traffic violation during the last five years in drivers' comprehension of 39 posted traffic signs in the city of Irbid, Jordan. These signs include 15 regulatory signs, 17 warning signs, and 7 guidance signs. A total of 400 paper-based surveys were completed by drivers with different socio-economic characteristics. Subsequently, a decision tree was created for each category of traffic signs to identify the most influential factors affecting drivers' comprehension. Each tree was created twice; once using the whole data set for building and validating the tree, and a second time only using 80% of the data for building and 20% for validating.ResultsThe accuracy of the generated trees in predicting drivers' comprehension of regulatory, guidance, and warning traffic signs was 70%, 71%, and 66.5%, respectively, when using the whole data for building and validating the tree, and was 65%, 62.5%, and 61.3%, respectively, when using only 80% of the data for building and the remaining for validating.ConclusionsThe generated decision trees showed that driving experience, marital status, age, and education background are the most influential factors in determining drivers' comprehension of traffic signs as they were primary splitters in such trees.Practical applicationThe rules obtained from the decision tree can be utilized by transportation agencies to determine the drivers who need help with understanding the road traffic signs.     

Fatigued driver’s driving behavior and cognitive task performance: Effects of road environments and road environment changes

This study aims to explore the effects of different road environments and their changes on driving behaviors and cognitive task performance of fatigued drivers. Twenty-four participants volunteered in a 2 (road environment) × 3 (fatigue level) within-subjects factorial design simulated driving experiment. Participants were asked to perform basic numerical calculation and distance estimation of traffic signs when driving normally, and provide answers to a questionnaire on fatigue rating. Results show that fatigued drivers faced greater attention demand, were less alert, and tended to overestimate the distance to roadside traffic signs. Fatigue caused by driving in complex road environment had the greatest negative impact on driving behavior and visual distance estimation, and the fatigue transfer effect worsened significantly but differently on both driving behavior and performance of fatigued drivers when switching from a complex to a monotonous road environment and vice versa. Notably, this study shows that fatigued drivers performed relatively better in arithmetic tasks than non-fatigued ones. In addition, when switching from a monotonous to a complex road environment, drivers’ performance in visual distance estimation and arithmetic tasks improved though their driving behavior deteriorated, revealing that the fatigue effect upon drivers might be explained to some extent by their alertness and arousal levels.