Insurance Classifications and Drivers' Galvanic Skin Response

Drivers' galvanic skin responses were recorded in town traffic and on country roads. Two groups of drivers were compared, those who would pay higher insurance premiums, by reason of their ago or occupation, and the rest. The high insurance group did not differ from the rest in the magnitude of the GSR. but when the ratio of the GSR in town traffic to that on a country road was considered there was a significant difference such that the high insurance group had a relatively greater GSR on the country road.

It is suggested that in town traffic most of the GSR arc generated by the actions of other drivers, whereas on the open road the GSRs are self generated by the subjects' driving. The GSR on the open road will therefore be more affected by the individual method of driving and by the risks the driver takes. The ratio of town GSR to country GSR can be used to eliminate the large unexplained differences between subjects and to expose the differences in driving behaviour     

Development of a statistical model to classify driving stress levels using galvanic skin responses

Many studies have demonstrated the strong relationships between physiological responses and driving stress, but they have done little to build a model that could be used to identify a driver's stress accurately in real time. The objective of this study is to develop a model that accurately classifies driving stress by monitoring physiological responses—specifically galvanic skin response (GSR). GSR data were collected from nine drivers with licenses obtained in the US in real road driving situations with two stress conditions—rest period (low stress) and highway or city driving (high stress). The validation drive was performed by one driver with licenses obtained in South Korea in real long‐term road driving situations with two stress conditions—rural area (low stress) and highway or highway under construction (high stress). Those two conditions were used to build a binary logistic regression model to classify low stress or high stress based on a driver's measured hand GSR. The overall classification accuracy of the developed model was found to be 85.3%, and the accuracy of cross validation, with a testing dataset, was found to be 83.2%. A simple logit model was developed to identify drivers' stress by incorporating their GSR data. The developed model can be embedded in a wearable device equipped with GSR sensors for drivers to detect their stress level in real time.     

Road-edge delineation in rural areas: effects on driving behaviour

When driving on lower-category Dutch rural roads without any delineation, drivers are likely to drift off the road with their right-side wheels, thus incurring damage to the pavement edge or even leading to accidents. In two experiments, two types of road-edge delineation, with continuous or dashed edge lines, were compared with two control roads without lines or with only a dashed line on the road axis. The first experiment consisted of non-obtrusive video recordings of passing traffic. Vehicle position on the experimental roads was more to the road's centre than on the control roads. The second experiment was a driving test with an instrumented vehicle, during daytime lighting and during darkness. Again, vehicle lateral position was more central on the experimental roads, especially during darkness. Subjects could safely pass oncoming vehicles. Driving speed increased on the experimental roads compared with the unlined control road, but not beyond speeds found on the axis-lined control road. Driver's mental effort while driving over the experimental roads did not differ from the effort while driving over the control roads. Subjectively rated effort was higher for the unlined control road than for the three other roads. Subjects preferred the edge-lined roads to the unlined control road, but not more than the axis-lined control road. It was concluded that edge-lines may provide a simple and effective way of inducing a more favourable lateral position on rural roads without having negative effects on subjective appraisal, driving performance or mental workload.     

Road-edge delineation in rural areas: effects on driving behaviour

When driving on lower-category Dutch rural roads without any delineation, drivers are likely to drift off the road with their right-side wheels, thus incurring damage to the pavement edge or even leading to accidents. In two experiments, two types of road-edge delineation, with continuous or dashed edge lines, were compared with two control roads without lines or with only a dashed line on the road axis. The first experiment consisted of non-obtrusive video recordings of passing traffic. Vehicle position on the experimental roads was more to the road's centre than on the control roads. The second experiment was a driving test with an instrumented vehicle, during daytime lighting and during darkness. Again, vehicle lateral position was more central on the experimental roads, especially during darkness. Subjects could safely pass oncoming vehicles. Driving speed increased on the experimental roads compared with the unlined control road, but not beyond speeds found on the axis-lined control road. Driver's mental effort while driving over the experimental roads did not differ from the effort while driving over the control roads. Subjectively rated effort was higher for the unlined control road than for the three other roads. Subjects preferred the edge-lined roads to the unlined control road, but not more than the axis-lined control road. It was concluded that edge-lines may provide a simple and effective way of inducing a more favourable lateral position on rural roads without having negative effects on subjective appraisal, driving performance or mental workload.     

Partially automated driving has higher workload than manual driving: An on-road comparison of three contemporary vehicles with SAE Level 2 features

Vehicles with SAE Level 2 automated features are already in active use on the road, and vehicles with Level 3 or 4 will be with us soon. Although the vehicles provide support for longitudinal and lateral control, partially automated driving experience is sometimes more demanding than manual driving. However, the effects of automated driving on workload in naturalistic conditions have not been extensively investigated, as most studies have been undertaken in driving simulators. This study aims to extend the current understanding about workload in partially automated driving on public roads. Drivers' perceived workload was assessed after conducting manual and automated driving activities using a small sample (N = 8). They performed driving tasks in three contemporary vehicles with SAE Level 2 features, in highway and urban environments. The comparative findings revealed that drivers' perceived workload was higher in partially automated driving than manual driving. Furthermore, perceived workload was higher in urban environments than highway environments and in less experienced drivers than more experienced drivers. Although the findings may need to be interpreted with caution due to the small sample size, they provide a future research agenda that can be built upon.     

基于电涡流传感器的车辆超速检测研究

车辆超速行驶是引发交通事故的重要因素。本文以单车道为例,在公路上相距一段距离埋设两个电涡流传感器,当汽车通过电涡流传感器时,产生脉冲信号,通过单片机编程对信号进行实时处理,最终得到汽车的行驶速度。实验表明,此方法可有效检测和记录各路段超速行驶的车辆,最终达到让驾驶员自觉遵纪守法、遵章驾驶和降低交通事故发生率,提高安全和畅通行车能力的目的。     

Angular and Metric Distance in Road Network Analysis: A nationwide correlation study

There is an epistemological divide in the field of road network analysis, concerning the way network distance should be conceptualized. On one hand, the generality of studies in the field adopt metric distance as a self-evident choice. On the other, space syntax studies adopt a different distance concept, namely that of angular distance, which ignores actual physical lengths. Theoretically, these two distance concepts imply quite different assumptions. Analytically, they produce also different results.In this paper we assess the differences between these two network distance concepts, using a model of the UK's complete road network (2,031,971 nodes) and a very large dataset of vehicular movement counts (20,752 locations). We describe the statistical associations between observed vehicular flows and the betweenness centrality of the road-network nodes where such flows were measured, the latter calculated with metric and angular distance functions, across a number of increasing radii. Relations to road capacity are also discussed in principal roads where this is known.The geographical comprehensiveness of our model and the size of our movement sample allow us to state, with unprecedented statistical validity, the clear outperformance of angular distance over metric distance, in what concerns the strength of the studied correlations. This is true for all types of roads (urban and non-urban) and for all motorized vehicles, representing 99.05% of the total traffic; the remaining 0.95% correspond to pedal cycles, which yielded inconclusive results. We also demonstrate the existence of two distinct regimes of association between movement and centrality, conspicuous in cities but altogether absent in non-urban areas, which represent new evidence supporting space syntax's dual model of urban form.     

Visual attention and the transition from novice to advanced driver

Inexperienced drivers are particularly vulnerable to road traffic accidents, and inattention emerges as a factor in these accidents. What do these drivers attend to and how can their observation skills be developed? When drivers scan the road around them, differences are observed as function of driving experience and training, with experienced drivers increasing their visual scanning on roadways of increasing complexity. Trained police drivers showed this effect of increased scanning even more than experienced drivers. This suggests that the driver's understanding of the task develops with experience, such that roads that demand increased monitoring (e.g. interweaving traffic on a multi-lane highway) receive more extensive scanning than roads that are simpler (e.g. light traffic on a straight rural road). Novice drivers do not show this sensitivity to road complexity, suggesting that they fail to attend to potential dangers involving the behaviour of other road users. Encouragingly, a simple training intervention can increase the visual scanning of novices.     

基于VR的驾驶仿真及安全教育系统开发

针对全国道路交通事故高发现状及传统驾驶安全教育方式单一、培训效果差的缺点,基于虚拟现实技术（VR）,在引发交通事故人为因素理论基础上,开发驾驶仿真及安全教育系统。系统基于Unity3D引擎,构建了基于道路实景数据的虚拟场景,并联合SUMO实现了道路交通流仿真,通过VR技术仿真驾驶环境及驾驶行为;基于碰撞检测原理,建立了关卡违规触发机制,编码自定义屏幕空间渲染方式模拟驾驶员视觉效果,并构建了基于图像的交通事故现场三维全景,从认知、感知层面培训驾驶员安全驾驶。实用性测试结果表明,系统实现了不同道路场景、气象条件与交通状况下的驾驶模拟及安全培训,增强了使用者的学习兴趣,提高了使用者驾驶安全素养,具有较强的实用性。     

Development of a measure model for optimal planning of maintenance and improvement of roads

In recent years, the spectacular increase of traffic in the majority of the developed countries has led to heightened concerns about the state of the roads, particularly in regard to how to improve driving conditions and, consequently, lower accident rates.There are many facets that influence the quality of roads, such as design characteristics (width, ditches, etc.) or their maintenance (signs, marking, etc.). All of them must be considered with a series of indicators when analyzing different roads.This work develops a methodology, based on the formulation of the Rasch model, that allows us to obtain an objective measure of road conditions, and later to evaluate each of the indicators that describe the road’s condition. As an example, data relating to the network of local and regional roads in the province of Badajoz, Spain, have been processed.Finally, results were implemented in a geographical information system (GIS) to visualize the distribution of all roads depending on their condition and, moreover, to make individual diagnoses of each road stretch possible, with the aim of optimizing resources allocation.     

Vehicle guidance by delineation systems at night

Abstract

Car and road designs are considered to be better when they require less driver attention and allow drivers to behave more as a supervisory controller in coping with normal, multi-task driving. In this paper an example is presented with respect to road design and, more specifically, with respect to various configurations of delineation on wet roads at night. The criteria for supervisory control are focused on drivers' observation strategy in terms of ‘free times’ available for tasks beyond lateral vehicle control, on drivers' control strategy in terms of amplitudes and frequencies of steering-wheel movements, and on the overall driving performance in terms of variations in lateral position and yaw rate.

The research findings on straight and curved roads recommend the necessity of using delineation devices at both sides of the traffic lane by the implementation of profiled stripes, raised pavement markers at the location of the centre and/or lane boundaries, or post-mounted delineators just beside the boundary. Post-mounted delineators on larger cross distances are less effective. The longitudinal spacing between the raised pavement markers has to be restricted up to 12 m for curves with radii for 200 m and up to 24 m for straight roads.     

The effect of grid size on street location time in maps

Two experiments investigating the time taken to locate roads in an urban map are described. The data support the hypotheses that road location time is (a) linear on the reciprocal of the distance between grid lines, and (b) linear on the number of roads per grid square. The issue of the optimal distance between grid lines is discussed and it is argued that for the particular map investigated, this distance should represent 0·25 km.     

基于空间六点不变量的道路立体目标识别方法

城市道路中常设置具有3D效果的平面路障或标志物,其具有高度的立体性和真实性,导致行人和辅助驾驶系统误判而造成严重事故,因此需要对道路立体目标进行识别,以获得真实路面情况.常见的射影不变量如交比是基于共面五点计算的,存在局限性,论文提出一种基于空间点元素的几何不变量计算方法,把空间元素的共点和共线用具有物理意义的量来表示...     

Effect of road layout and road environment on driving performance,drivers' physiology and road appreciation

Abstract

Infrastructural changes were implemented on rural 80km/h roads in The Netherlands in an effort to reduce speeding. The road infrastructure changes were designed to produce discomfort for the speeding driver by providing noxious auditory and haptic feedback. On experimental roads, smooth-surface road width was reduced by using blocks of gravel chippings placed along the centre line and at intervals on road edges. It was predicted that these changes would increase mental load while driving, and thereby decrease speeding. In a field experiment 28 subjects drove an instrumented vehicle over experimental and control roads. A decrease in driving speed and swerving behaviour was found on the experimental roads, and this was coupled with a decrease in heart rate variability, consistent with an increase in mental load. Roads in two different road-side environments (woodland vs. moorland) were also tested. There were differences in driver appraisal of the two environments, but no interactions were observed between these appraisals and driving performance on the experimental roads. It is concluded that the infrastructural measures have a useful role to play in road safety through a reduction in driver speeding.     

Driver information system: a combination of augmented reality,deep learning and vehicular Ad-hoc networks

Improving traffic safety is one of the important goals of Intelligent Transportation Systems (ITS). In vehicle-based safety systems, it is more desirable to prevent an accident than to reduce severity of injuries. Critical traffic problems such as accidents and traffic congestion require the development of new transportation systems. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. One of the solutions to prevent accidents is to provide information on the surrounding environment of the driver. Augmented Reality Head-Up Display (AR-HUD) can facilitate a new form of dialogue between the vehicle and the driver; and enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads. In this paper, we propose a fast deep-learning-based object detection approaches for identifying and recognizing road obstacles types, as well as interpreting and predicting complex traffic situations. A single convolutional neural network predicts region of interest and class probabilities directly from full images in one evaluation. We also investigated potential costs and benefits of using dynamic conformal AR cues in improving driving safety. A new AR-HUD approach to create real-time interactive traffic animations was introduced in terms of types of obstacle, rules for placement and visibility, and projection of these on an in-vehicle display. The novelty of our approach is that both global and local context information are integrated into a unified framework to distinguish the ambiguous detection outcomes, enhance ITS by superimposing surrounding traffic information on the users view and keep drivers view on roads.     

基于改进的渐进纹理分析技术提取IKONOS影像中的城市主要道路

提出了一种从IKONOS多光谱影像提取城市主要道路的方法。首先对影像进行3个层次的纹理分析。第一层为检测集像元与训练集像元在波段空间中的闵氏距离;第二层为检测集像元及其3×3窗口内像元分布与训练集像元在波段空间中的巴氏距离;第三层为检测集像元及其3×3窗口内像元分布与训练集像元在彩色纹理特征空间中的巴氏距离。对上述获取的结果分别进行了阈值分割、细化,并结合道路的几何特征,采用模糊数学的方法对各个图层进行了融合。接着提出了一种基于道路知识的道路段连接算法。最后用多项式拟合方法对连接结果进行了优化,获得了较好的提取结果。     

Alcohol effects on driver performance under conditions of divided attention

Laboratory studies have indicated that deleterious effects of alcohol on human performance are most evident when an individual is required to monitor sensory input relevant to the concurrent performance of two activities. Consequently, data collected during an in-depth study of metropolitan road accidents were examined to determine to what extent the accidents of intoxicated drivers were characterized by the driver's attention, prior to the accident, being directed to some source of information input secondary to the driving task. These data indicated that intoxicated drivers were more likely than others to have been engaged in some pre-accident activity that was secondary to the driving task. A number of mechanisms that may underlie the apparent interactive effects of alcohol and secondary activity on driving were outlined for future experimental consideration.     

基于路网结构的城市交通事故短期风险预测方法

城市交通事故一般都发生在公共道路上,然而现有的交通事故风险预测算法都通过对预测区域进行规则网格化来确定预测空间单位,导致预测精度不高且实用价值较低。本文将道路路段作为预测单位,采用图卷积和长短期记忆网络,构建了一种基于路网结构的城市交通事故短期风险预测方法(traffic accidents risk prediction based on road network,TARPBRN)。该方法能对指定路段短期内的交通事故风险进行预测,从而可以有针对性地进行治理,减少交通事故的发生。本文使用杭州市西湖区的交通事故数据对模型进行了训练,并与4种常用的计量经济学模型和3种已有的深度学习预测算法进行了对比。实验结果证明本文算法在准确度、正确率和漏报率等方面都优于已有算法。     

A Computer Vision System for Lateral Localization

For urban driving, knowledge of ego‐vehicle's position is a critical piece of information that enables advanced driver‐assistance systems or self‐driving cars to execute safety‐related, autonomous driving maneuvers. This is because, without knowing the current location, it is very hard to autonomously execute any driving maneuvers for the future. The existing solutions for localization rely on a combination of a Global Navigation Satellite System, an inertial measurement unit, and a digital map. However, in urban driving environments, due to poor satellite geometry and disruption of radio signal reception, their longitudinal and lateral errors are too significant to be used for an autonomous system. To enhance the existing system's localization capability, this work presents an effort to develop a vision‐based lateral localization algorithm. The algorithm aims at reliably counting, with or without observations of lane‐markings, the number of road‐lanes and identifying the index of the road‐lane on the roadway upon which our vehicle happens to be driving. Tests of the proposed algorithms against intercity and interstate highway videos showed promising results in terms of counting the number of road‐lanes and the indices of the current road‐lanes.     

Some factors affecting motorcyclists' conspicuity

Three experiments examined some of the factors that might affect motorcyclists' conspicuity to other road users. In each case, subjects saw a sequence of slides showing traffic, some of which contained a motorcyclist. A record was taken of their reaction times to decide whether or not a motorcyclist was present in each slide. Experiments 1 and 2 examined the effects on conspicuity of headlight use, type of clothing, distance of motorcyclist from viewer, and driving situation (urban or semi-rural). Experiment 3 looked more closely at environmental influences on motorcyclist conspicuity, systematically varying the level of background ‘clutter’ behind the motorcyclist. All three experiments indicate that the effectiveness of the conspicuity aids used, especially clothing, may depend on the situation in which the motorcyclist was located: bright clothing and headlight use may not be infallible aids to conspicuity. Brightness contrast between the motorcyclist and the surroundings may be more important as a determinant of conspicuity than the motorcyclist's brightness per se. Motorcyclists' conspicuity is a more complex issue than has hitherto been acknowledged.