Age differences in visual search for traffic signs during a simulated conversation

The effects of divided attention were examined in younger adults (M = 23 years) and older adults (M = 64 years) who searched for traffic signs in digitized images of traffic scenes. Sign search was executed under single-task and dual-task conditions in scenes containing either small or large amounts of visual clutter. For both age groups, clutter and the secondary task had additive effects on search accuracy, speed, and oculomotor involvement. Compared with the younger adults, older adults were less accurate, especially with high-clutter scenes, were slower to decide that a target sign was not present, and exhibited a marginally greater divided-attention effect on reaction times. They exhibited longer fixations in the divided-attention condition, in which they also showed a disproportionate reduction in recognition memory for the content of the secondary task. Actual or potential applications of this research include methods for evaluating the distraction of conversations and safety implications of conversation on visual search behavior.     

The relation between driving experience and recognition of road signs relative to their locations

OBJECTIVES: Examine how driving experience and expectations affect the ability of experienced drivers to identify traffic signs--specifically, no right turn (NRT) and no left turn (NLT) at intersections. BACKGROUND: Failure to heed signs is a frequent cause of accidents, and the authors focused on the contributions of experience and expectancy to sign identification. METHOD: Inexperienced and experienced drivers were connected to an eye tracker system and briefly exposed to various traffic scenes. Some of the pictures included an NRT sign at the expected location (on the right), and some included the same sign at an unexpected location (on the left). The same procedure was used with an NLT traffic sign. RESULTS: Experienced drivers identified traffic signs better than inexperienced drivers did when the signs were posted at the expected location but identified them worse than did inexperienced drivers when they were at unexpected locations. CONCLUSIONS: With experience, drivers' expectations regarding the expected location of traffic signs become so strong that violating these expectancies results in more identification errors among experienced drivers than among inexperienced drivers. To optimize experienced drivers' traffic sign identification, signs must be located in accordance with drivers' expectations--specifically, on the right side of the road. APPLICATIONS: When signs are misplaced, crashes can be caused by inappropriate placement rather than inappropriate driving. Highway designers should ensure that their design conforms to standards that shape experienced drivers' expectations.     

Design of a visual perception model with edge-adaptive Gabor filter and support vector machine for traffic sign detection

Traffic sign detection is a useful application for driving assistance systems, and it is necessary to accurately detect traffic signs before they can be identified. Sometimes, however, it is difficult to detect traffic sign, which may be obscured by other objects or affected by illumination or lightning reflections. Most previous work on this topic has been based on region of interest analysis using the color information of traffic signs. Although this provides a simple way to segment signs, this approach is weak when a sign is affected by illumination or its own color information is distorted. To overcome this, this paper introduces a robust traffic detection framework for cluttered scenes or complex city views that does not use color information. Moreover, the proposed method can detect traffic sign in the night. We establish an edge-adaptive Gabor function, which is derived from human visual perception. It is an enhanced version of the original Gabor filter, and filters out unnecessary information to provide robust recognition. It decomposes the directional information of objects and reflects specific shapes of traffic signs. Once the extracted feature is obtained, a support vector machine detects the traffic sign. Applying scale-space theory, it is possible to resolve the scaling problem of the objects that we want to find. Our system shows robust performance in traffic sign detection, and experiments on real-world scenes confirmed its properties.     

Eye movements of younger and older drivers.

This 2-part study focuses on eye movements to explain driving-related visual performance in younger and older persons. In the first task, participants' eye movements were monitored as they viewed a traffic scene image with a numeric overlay and visually located the numbers in their sequential order. The results showed that older participants had significantly longer search episodes than younger participants, and that the visual search of older adults was characterized by more fixations and shorter saccades, although the average fixation durations remained the same. In the second task, participants viewed pictures of traffic scenes photographed from the driver's perspective. Their task was to assume the role of the driver and regard the image accordingly. Results in the second task showed that older participants allocated a larger percentage of their visual scan time to a small subset of areas in the image, whereas younger participants scanned the images more evenly. Also, older participants revisited the same areas and younger participants did not. The results suggest how aging might affect the efficacy of visual information processing. Potential applications of this research include training older drivers for a more effective visual search, and providing older drivers with redundant information in case some information is missed.     

交通标志识别方法综述

交通标志识别对于车辆安全行驶具有重要作用，特别是针对存在光照变化和遮挡的情况，准确性高、实时性好的交通标志自动识别亟需解决。对交通标志的检测和交通标志的识别分别进行了综述，给出了它们的原理、步骤、特点和性能，以及进行算法研究常用的交通标志数据库。相比于传统的特征检测和识别方法，采用深度学习有助于解决光照变化、部分遮挡等情况下的交通标志识别难题，是今后自动驾驶和无人驾驶性能提升的主要途径。     

自然场景下交通标志的自动识别算法

对自然场景下的交通标志进行准确的识别分类,是智能汽车的重要组成部分。交通标志本身数量众多,其形态复杂,而自然场景下存在着光照变化、褪色污损、扭曲变形等情况对交通标志表观的影响,为自动识别带来了很大的挑战。本文在分析交通标志特征的基础上,将Adaboost算法与SVM算法融合用于自然场景下的交通标志识别。实验证明,该方法具有较高的识别率和较快的识别速度,在智能汽车系统中具有较高的应用价值。     

改进SSD算法在交通标志检测中的应用

针对交通标志检测存在误检率高、鲁棒性差等问题,提出了一种改进SSD(single shot multibox detector)的交通标志检测方法。首先从不同维度提取交通标志的位置和方向感知信息,改善目标在浅层特征图上的感受野区域。其次使用特例化的卷积内核对深层特征图进行条件参数卷积,增强交通标志的特征表达能力。最后对通道注意力机制进行改进,在特征通道中融入目标空间信息,提升交通标志目标的显著性。实验结果表明,提出的方法相较于原始SSD在CCTSDB数据集上的检测精度提升了7.6个百分点,检测速度达到87.5 FPS;在LISA数据集上的平均准确率为94.6%,检测速率为85.0 FPS。相比于其他的检测方法,改进后的SSD算法在复杂的自然场景中对交通标志具有更好的鲁棒性。     

Do in-vehicle advanced signs enhance older and younger drivers’ intersection performance? Driving simulation and eye movement results

An experimental study was conducted to determine if intersection behaviour benefited from advanced in-vehicle signs presented to older and younger drivers in a head-up display (HUD) format. The University of Calgary Driving Simulator (UCDS) was used to evaluate intersection performance. Measures of those who were able to stop or ran the yellow light, speed over the span of the intersection, perception response time, and eye movements were analyzed to determine if performance improved or whether undesirable adaptive behaviours occurred. In-vehicle signs facilitated an increase in the frequencies of stopping for both younger and older drivers at intersections with relatively short yellow onsets. The speed at the yellow light onset for both those who stopped and those who proceeded through the intersection was reduced by the presence of the in-vehicle signs. The primary behavioral influence of the in-vehicle signs was to cause the drivers’ to reduce their velocity in advance of an intersection. Eye movement analyses indicated that younger drivers looked at the in-vehicles signs more often and for longer overall durations than older drivers. Older drivers had slower intersection approach speeds, stopped more accurately, and were more likely to not clear the intersection before the traffic light turned to an all-red phase than younger drivers. The implications of the in-vehicle sign results are discussed in terms of in-vehicle information systems (IVIS) design guidelines and evaluation methods.     

Rectification of legibility distance in a driving simulator

Visual differences lead to differences in the legibility distances of traffic signs between driving simulators and real road environments. To ensure that the legibility distance in a simulator is similar to that in the real world, this study proposes a theoretical equation for predicting legibility distance and a simple algorithm for determining the magnifying power of a traffic sign for a display system in a simulator. Experiments of traffic sign recognition using a simulator were conducted under quasi-static and dynamic driving conditions. On-road tests were also carried out under quasi-static and dynamic driving conditions. Thirty healthy and non-disabled volunteers were recruited. The experimental results showed that the proposed theoretical equation for predicting legibility distance and the simple algorithm for determining the magnifying power of traffic signs reduced the difference in legibility distances between the simulator and real road environment under quasi-static and dynamic driving conditions.     

复杂环境下的交通标志检测与识别方法综述

交通标志检测与识别是无人驾驶三大模块中环境感知的研究热点之一,检测和识别交通标志可以向无人车传递道路交通信息,优化行车决策。在暴雨、大雾以及光线昏暗等复杂环境下,拍摄到的图像往往会被遮挡,变得模糊。这不仅影响图像的质量,还会对后期标志的检测与识别带来巨大的困难。简述了交通标志检测与识别方法,对近年来国内外学者解决各类复杂环境下交通标志检测与识别的方法、原理和步骤进行了总结归纳,有利于人们更好地解决此类问题。同时,对常用的交通标志数据集进行了总结,并对数据集里在复杂环境下拍摄的图像比例给予了说明。     

基于多尺度特征提取与特征融合的交通标志检测

基于卷积神经网络的交通标志检测算法在对现实中复杂的交通场景图像进行交通标志检测时,难以同时解决定位和分类两项任务,并且目标检测领域相关算法所使用的公开数据集提供的图像和交通标志的种类不能满足现实交通场景中复杂的情况。建立一个新的道路交通标志数据集,在YOLOv4算法的基础上针对现实交通场景图像的复杂性和图像中交通标志尺寸差异较大的特点,设计多尺寸特征提取模块和增强特征融合模块,提高算法同时定位和分类交通标志的能力。在此基础上,对算法中不同的模块设置不同的参数进行对照实验,得到一组表现最优的参数,用于检测现实交通场景图片中的交通标志。在道路交通标志数据集上的实验结果表明,该算法相比基于卷积神经网络的同类型任务目标检测算法具有更高的检测精度,平均精度均值达到83.63%。     

The ergonomics of road signs: explicit and embedded speed limits

The European traffic sign system encodes the meaning of a sign by use of varying forms, colours and symbols. However, signs differ greatly in how explicit their message is. Thus, the meaning of most direction signs is self-evident, while that of some others is less obvious. The European built-up area sign limits driving speed to 50 km/h, like speed limit signs, but its rectangular form and symbol not associated with speed limit distinguish it clearly from standard speed limit signs. Also, the function of the built-up area sign is more complex than the one of speed limit signs: the built-up area sign both limits speed and informs about entering a built-up area. The purpose of this study was to investigate whether the built-up area sign is noticed and obeyed as well as speed limit signs. In three field experiments the 50 km/h, 80 km/h signs and built-up area signs were alternated and speed changes were measured. Results indicated that the built-up area signs elicit only negligible speed responses compared to the speed limit signs while supervision (visible police car) results in a similar effect for both signs. The same results were also obtained in the laboratory where slides about traffic situations were shown to subjects who were asked if it was necessary to decelerate. These findings suggested that, in contrast to the built-up area sign, drivers have developed a mental model which facilitates more effective noticing of speed limit signs. The built-up sign does not share these facilitating effects, because of its dual function and symbol and form which are not associated with speed limits.     

A driving-emulation task to study the integration of goals with obligatory and prohibitory traffic signs

This research aims to analyse how drivers integrate the information provided by traffic signs with their general goals (i.e. where they want to go). Some previous studies have evaluated the comparative advantages of obligatory and prohibitory traffic signs using a judgement task. In this work, a new experimental task with greater similarity to driving situations is proposed. Participants imagine they are driving a vehicle and must make right or left turn manoeuvres according to a previously indicated objective and the information from obligatory and prohibitory traffic signs. Eighty-two participants took part in two different experiments. According to the results, an obligatory traffic sign is associated with faster and more accurate responses only when the participant's initial objective is allowed. When the initial objective was not allowed, an advantage in accuracy was observed with prohibitory traffic signs and there was no significant difference in reaction time between the two types of sign. These results suggest that having an obligatory traffic sign may facilitate a correct response when the driver's goal is effectively allowed, whereas a prohibitory traffic sign could be more effective in preventing error when the driver has a not-allowed goal in mind. However, processing a prohibitory sign requires an extra inference (i.e. deciding which is the allowed manoeuvre), and thus the potential advantage in reaction time of the prohibitory sign may disappear. A second experiment showed that the results could not be explained by a potential congruency effect between the location (left or right) of the road signs and the position of the key or the hand used to respond (such as the Simon effect or the spatial Stroop effect). Also, an increase in the difficulty of the task (using an incongruent hand to respond) affected performance more strongly in experimental conditions that required making inferences. This made the advantage of the obligatory sign over the prohibitory sign in this condition more noteworthy. The evidence gathered in the current study could be of particular interest in some applied research areas, such as the assessment of road traffic signalling strategies or the ergonomic design of GPS navigation systems.     

三角形交通标志的智能检测方法

基于交通标志都具有一定的颜色和形状，以红色倒三角形交通标志为例，介绍一种新的智能检测方法。该方法主要由以下4个环节构成。首先是颜色提取，将图像转换到HSI颜色模型的色调子空间，提取特定颜色——红色；其次是边缘检测，使用标准LOG模板在提取出来的红色区域上进行；这样，计算量将大为减少；然后是顶点判别，对边缘上的这些点，采用神经网络分类器进行分类，并分别对三角形的3种不同顶点作标记；最后是三角形的定位，通过一定的匹配准则提取出三角形。对不同场景下的20幅图像进行测试，检测正确率达到了100％。实验结果表明，该方法具有鲁棒性好、速度快、检测准确率高等特点。     

Motion extrapolation of car-following scenes in younger and older drivers

OBJECTIVE: The objective was to determine whether distortions occur in motion extrapolation of car-following scenes in younger and older drivers. BACKGROUND: Drivers cannot see an entire traffic scene clearly with one glance. They must extrapolate the motion of surrounding vehicles while scanning other parts of the scene. Further, abilities relevant to motion extrapolation decline with age. Thus, it is important to evaluate age differences in motion extrapolation. METHOD: Displays simulated car-following scenes. After an interruption, the scene reappeared at the correct position in its trajectory or at a position that was more advanced or less advanced than the correct position. Drivers reported whether the scene reappeared at the correct position. RESULTS: When the virtual self moved faster than the lead car, older drivers' judgments were biased toward less advanced reappearance positions. Younger drivers' judgments were biased toward more advanced positions. The implication is that older drivers extrapolated the motion slower than did younger drivers. CONCLUSION: Distortions occurred in motion extrapolation of car-following scenes, and age differences occurred in such distortions. APPLICATION: Potential applications of this research include traffic safety. Age differences in motion extrapolation are useful to consider in differential accident patterns of younger and older drivers. Future research should investigate the relationship between accidents and the ability to extrapolate motion, particularly during driver distractions.     

改进YOLOv7的交通标志检测算法

自动驾驶技术的快速发展,导致对交通标志检测技术的要求日益提高.为解决YOLOv7算法在识别小目标时误检、漏检等问题,本文提出一种基于注意力机制的交通标志检测模型YOLOv7-PC.首先通过K-means++聚类算法对交通标志数据集进行聚类,获得适用于检测交通标志的锚框;其次在YOLOv7主干特征提取网络中引入坐标注意力机制,将交通标志的横向和纵向信息嵌入到通道中,使生成的特征信息具有交通标志的坐标信息,加强有效特征的提取;最后在加强特征提取网络中引入空洞空间金字塔池化,捕获交通标志多尺度上下文信息,在保证交通标志小目标分辨率的同时,进一步扩大卷积的感受野.在中国交通标志检测数据集(CCTSDB)上的实验表明,本文算法增强了识别小目标的能力,相较于YOLOv7模型,本文算法的m AP、召回率平均分别提高了5.22%、9.01%,是一种有效的交通标志检测算法.     

基于多模型表示的交通标志识别算法设计

通过变换 RGB 空间颜色值并分割图像,利用标记图为特征的形状分类器检测城市环境中的交通标志.为了提高分类准确度,用两种模型表示方法分类交通标志:1)结合二元树复小波变换和二维独立分量分析提取特征,送入最近邻分类器中分类交通标志;2)提取交通标志的内部图形,利用模板匹配进行分类.最后,将两种分类结果融合输出.实验结果表明,所提出算法的整体识别率超过91%,平均处理帧率达到6.6帧/s,系统能够鲁棒、有效和实时地识别交通标志.     

Older observers' tolerance of optical blur: age differences in the identification of defocused text signs

To determine if visual aging affects the ability to identify blurred text signs in daytime or nighttime viewing conditions, the photopic and mesopic legibility thresholds of young and old adult observers were compared at three levels of optically induced acuity (Experiment 1). For both age groups, legibility was reduced by nighttime luminance and degraded acuity. Surprisingly, older observers were better than younger ones in identifying defocused (optically blurred) text in both daytime and nighttime conditions. In Experiment 2, older observers were also superior to younger ones in identifying defocused standard and spatially matched novel text signs. These findings indicate that older observers' tolerance of optical blur is a generic ability, rather than one explained by familiarity with the low-pass optical profile of specific signs. Consistent with the notion that factors beyond acuity contribute more importantly to text legibility for older than for younger observers, acuity was a stronger predictor of legibility thresholds in the younger group. Actual or potential applications of this research include the need to consider functional acuity in visual screening protocols and the design of text displays, as well as the utilization of compensatory letter-recognition training for older observers or others with diminished acuity.     

Driving comfort,enjoyment and acceptance of automated driving – effects of drivers’ age and driving style familiarity

Automated driving has the potential to improve the safety and efficiency of future traffic and to extend elderly peoples’ driving life, provided it is perceived as comfortable and joyful and is accepted by drivers. Driving comfort could be enhanced by familiar automated driving styles based on drivers’ manual driving styles. In a two-stage driving simulator study, effects of driving automation and driving style familiarity on driving comfort, enjoyment and system acceptance were examined. Twenty younger and 20 older drivers performed a manual and four automated drives of different driving style familiarity. Acceptance, comfort and enjoyment were assessed after driving with standardised questionnaires, discomfort during driving via handset control. Automation increased both age groups’ comfort, but decreased younger drivers’ enjoyment. Younger drivers showed higher comfort, enjoyment and acceptance with familiar automated driving styles, whereas older drivers preferred unfamiliar, automated driving styles tending to be faster than their age-affected manual driving styles.

Practitioner Summary: Automated driving needs to be comfortable and enjoyable to be accepted by drivers, which could be enhanced by driving style individualisation. This approach was evaluated in a two-stage driving simulator study for different age groups. Younger drivers preferred familiar driving styles, whereas older drivers preferred driving styles unaffected by age.     

The effectiveness of glare-reducing glasses on simulated nighttime driving performance in younger and older adults

Glare from oncoming headlights is a problem for nighttime drivers because it can decrease visual acuity and cause discomfort. This diminished visual ability and discomfort due can increase drivers’ risk for traffic accidents. Older drivers experience more severe detrimental effects from nighttime glare, and these effects may pose a growing roadway hazard as the number of older drivers increases. The increased brightness of popular high-intensity-discharge (HID) headlights may further exacerbate these visibility problems. In a sample of younger (under 40 years of age) and older (40 years of age and older) drivers, we examined the impact of headlight glare from HID and traditional halogen lights on driving performance in a simulator, as well as the effectiveness of novel polarized glare-reducing eyeglasses for mitigating glare-induced performance deficits. The glare-reducing glasses increased visual awareness in the face of oncoming HID headlights compared to halogen headlights in both age groups. Older drivers performed significantly worse than did younger drivers on several measures of driving and visual detection performance. The glare-reducing glasses mitigated performance deficits, with older drivers performing similarly to younger drivers when exposed to HID headlights while wearing the polarized glasses. Due to the introduction of brighter LED-based headlights to the consumer automotive market and an expanding population of older drivers, automotive manufactures should consider glare-mitigation strategies when designing future headlight systems.