基于物联网的高速公路主动发光诱导系统设计

近年来,交通压力与日俱增,高速公路的安全问题也日益凸显。研究表明,低能见度环境会导致交通安全事故发生机率提升。因此,提高高速公路低能见度环境下交通诱导水平从而提升交通运行的安全性愈加重要。为了解决上述问题,提出了一种基于物联网的高速公路路侧主动发光诱导系统。该系统基于智能检测技术和局域网构建技术,通过协同短距离Zigbee通信技术和长距离光纤通信技术,构建高速公路智能化监测控制网络,通过车-路协同控制路侧发光诱导装置对低能见度环境下的高速运行车辆进行有效诱导,提高高速公路的交通安全性。通过模拟实验,该系统能够大大降低设备铺设路段交通安全事故的发生率,且相比于原有LED发光设备,该系统也具有更好的节能效果。     

Ergonomics solution for crossing collisions based on field assessment of visual environment at urban intersections in Japan

This paper aims to assess quantitatively the actual visual environment of uncontrolled urban downtown intersections in Japan in relation to frequently occurring crossing collisions and to discuss the safety countermeasures for them. In Field Study 1 dealing with direct visibility, our ultra-wide-angle photograph analysis revealed that most of the right/left-ward visible range at 11 intersections were insufficient to check safety, and the quality of direct visibility was closely associated with causing crossing collisions. The countermeasures to reduce a blind area were determined to be a top priority. In Field Study 2 dealing with indirect visibility, more than half of the 25 traffic convex mirrors had marked shortcomings for preventive safety, and ergonomics guidelines ensuring indirect visibility were proposed for installing traffic convex mirrors. Low-cost/low-technology-oriented countermeasures are highly recommended to obtain clear/sufficient images of crucial information satisfying drivers' requirements on traffic convex mirrors in accordance with those ergonomics guidelines was highly recommended. Crossing collisions could be prevented by improvement of poor direct and indirect visibility.     

3D road traffic situation simulation system

The 3D traffic situation simulation system combines the multibody based mathematical model of a vehicle, the multibody mathematical model of human body, the database of vehicle and human body data and the display subsystem. Together with the model of driving surface the system can be used to simulate and analyse vehicle and its occupant behaviour under different road conditions and different driving regimes. The result obtained this way can be used to investigate safety related parameters and optimise the driver–vehicle–road system regarding to arbitrary criteria (safety, comfort, speed, etc.). The results of simulations are available as numerical data as well as animations in virtual 3D environment.     

考虑能见度影响的公路隧道照明动态优化与智能控制

为解决不同能见度影响下公路隧道实际路面亮度变化过大以及由此引起的行车安全与能源虚耗问题, 本文提出了一种能够改善公路隧道照明环境的动态优化与智能控制方法. 首先, 通过对不同时空条件下的公路隧道进行现场试验和数据分析, 得到了隧道内能见度的变化规律; 其次, 在公路隧道传统照明设计的基础上考虑能见度对照明环境的影响, 建立了基于隧道内能见度、交通量、车速、路面亮度和照明亮度的按需照明与动态优化模型;随后, 以不同地区公路隧道的实测数据为样本, 结合划分出的公路隧道典型照明场景和模糊径向基神经网络算法构建了公路隧道照明智能控制模型, 最后, 通过仿真实验验证了所构建模型的有效性, 其结果表明, 本文所提出的优化控制方法能够在保证隧道照明安全性的前提下兼顾节能性.     

基于视频信息的城市路段交通安全状态评估方法研究

城市道路交通安全状态实时评估是智能交系统的重要研究内容。针对现有交通安全状态实时评估方法的评估效果不理想的问题,建立了基于视频信息的城市路段交通安全状态评估方法。首先,分析了基于视频信息的交通流参数快速检测方法;然后,从车速离散度的角度提出了影响城市路段交通安全状态的路段车速离散度的概念;最后,建立了基于路段车速离散度的城市路段交通安全状态评估方法。实验表明,所提方法能够实时合理地对城市道路的安全状态水平进行评估,评估结果可以为交通管理部门制定有效的城市路段交通安全改进方案提供相应的依据。     

Image-based retro-reflectivity measurement of traffic signs in day time

The visibility of a traffic sign at night depends on its retro-reflectivity, a property that needs to be frequently monitored to ensure transportation safety. In the U.S., Federal Highway Administration (FHWA) maintains regulations to ensure minimum retro-reflectivity levels. Current measurement techniques either (a) use vehicle mounted device during the night, or (b) use manual handheld devices during the day. The former is expensive due to nighttime labor cost. The latter is time-consuming and unsafe. To address these limitations, this paper presents a computer vision-based technique to measure retro-reflectivity during daytime using a vehicle mounted device. The presented algorithms simulate nighttime visibility of traffic signs from images taken during daytime and measure their retro-reflectivity. The technique is faster, cheaper, and safer as it neither requires nighttime operation nor requires manual sign inspection. It also satisfies FHWA measurement guidelines both in terms of granularity and accuracy. The performance of the presented technique is evaluated under various testing conditions. The results are promising and demonstrate a strong potential in lowering inspection cost and improving safety in practical applications on retro-reflectivity measurement.     

Evaluating traffic effects of a route guidance system by dynamic simulation

This paper will present how to evaluate the global traffic effect of a telematic route guidance system by using a developed simulation tool, DYNEMO. A mesoscopic model, that gathers the merits of both macroscopic and microscopic traffic models, has been applied in this tool for dynamic traffic simulation. The communication between vehicles and the telematic infrastructure inside a road network is simulated by decision points that able to give last traffic messages, as vehicles pass by them. The evaluation will be made from the traffic aspect, safety aspect and consumption as well as emission. We will illustrate the approach by using the city road network of Braunschweig in Germany as an example of implementing a kind of route guidance system like EURO-SCOUT.     

Some chain visibility problems in a simple polygon

In this paper, the notions of convex chain visibility and reflex chain visibility of a simple polygonP are introduced, and some optimal algorithms concerned with convex- and reflex-chain visibility problems are described. For a convex-chain visibility problem, two linear-time algorithms are exhibited for determining whether or notP is visible from a given convex chain; one is the turn-checking approach and the other is the decomposition approach based on checking edge visibilities. We also present a linear-time algorithm for finding, if any, all maximal convex chains of a given polygonP from whichP is visible, where a maximal convex chain is a convex chain which does not properly include any other convex chains. It can be made by showing that there can be at most four visible maximal convex chains inP with an empty kernel. By similar arguments, we show that the same problems for reflex chain visibility can be easily solved in linear time.     

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.     

Some chain visibility problems in a simple polygon

In this paper, the notions of convex chain visibility and reflex chain visibility of a simple polygonP are introduced, and some optimal algorithms concerned with convex- and reflex-chain visibility problems are described. For a convex-chain visibility problem, two linear-time algorithms are exhibited for determining whether or notP is visible from a given convex chain; one is the turn-checking approach and the other is the decomposition approach based on checking edge visibilities. We also present a linear-time algorithm for finding, if any, all maximal convex chains of a given polygonP from whichP is visible, where a maximal convex chain is a convex chain which does not properly include any other convex chains. It can be made by showing that there can be at most four visible maximal convex chains inP with an empty kernel. By similar arguments, we show that the same problems for reflex chain visibility can be easily solved in linear time.     

Using the Event Analysis of Systemic Teamwork (EAST) to explore conflicts between different road user groups when making right hand turns at urban intersections

Collisions between different types of road users at intersections form a substantial component of the road toll. This paper presents an analysis of driver, cyclist, motorcyclist and pedestrian behaviour at intersections that involved the application of an integrated suite of ergonomics methods, the Event Analysis of Systemic Teamwork (EAST) framework, to on-road study data. EAST was used to analyse behaviour at three intersections using data derived from an on-road study of driver, cyclist, motorcyclist and pedestrian behaviour. The analysis shows the differences in behaviour and cognition across the different road user groups and pinpoints instances where this may be creating conflicts between different road users. The role of intersection design in creating these differences in behaviour and resulting conflicts is discussed. It is concluded that currently intersections are not designed in a way that supports behaviour across the four forms of road user studied. Interventions designed to improve intersection safety are discussed.

Practitioner Summary: Intersection safety currently represents a key road safety issue worldwide. This paper presents a novel application of a framework of ergonomics methods for studying differences in road user behaviour at intersections. The findings support development of interventions that consider all road users as opposed to one group in isolation.     

The design and implementation of a road safety relational database

Road safety can be measured by the rate of road traffic accidents that occur periodically. In developing countries, one major problem confronting researchers in the area of road safety is that the sources of data collection are not centrally controlled. The proliferation of the sources of data collection leads to data incompatibility, incorrectness, inconsistency and redundancy. These, in turn, lead to inaccurate and conflicting statistics generated and published by different research groups. In this paper, a relational database to support road safety system and a technique for its implementation are presented. It is shown how the database can be processed and accurate statistics about road safety generated and analysed to support planning, decision making and control of operations by the authorities involved in road safety.     

交通网络车流量的分布式协同优化方法研究

针对城市交通网络主干道车流量密度非连续特性,分析了交通网络车流量分段仿射模型.从最小化车流量延迟角度,给出了优化目标函数并进行凸分析.在此基础上,将城市交通网络系统描述为非完整性约束条件的非线性动态系统,利用反步法通过控制变量代换和状态转换将该系统转化为时变可控协同标准型的一般形式,并设计城市交通网络系统的分布式协同控制律.然后在城市主干道交通网络模型中,分别对协同分布式优化方法、定时控制、感应控制三种交通控制算法进行对比仿真分析,从仿真结果可以看出,本文所提出的协同分布式优化方法具有较好的性能,有效的降低了交通网络中车辆平均等待时间.     

基于AlexNet算法的道路能见度估测方法

本文采用AlexNet神经网络算法构建一个高速公路能见度识别的框架，通过对道路摄像头图像的采集，对图像进行标注、对AlexNet算法进行训练，提取图像能见度特征，构建能见度等级识别模型，实时接入道路摄像头图像，实现能见度值的估测。通过对安徽省高速公路42个监控摄像机进行图像的采集，抽取标注有能见度值的15万余幅样本，进行能见度识别结果分析，结果显示平均识别率达到78.02%，其中有14个站点的识别率超过90%，21个站点的识别率在80%以上。基于AlexNet算法的道路能见度估测方法能够满足道路能见度实时性和识别准确率的要求，可以作为能见度仪未安装地区的能见度辅助监测方法，同时对于光照变化、远近距离等都具有良好的鲁棒性。     

道路交通事故径向基神经网络预测模型研究

道路交通事故预测的目的是为交通管理部门提供未来交通安全的发展趋势。论文在进行交通事故统计的基础上,运用径向基神经网络理论,利用Matlab软件建立交通事故预测模型,对1990年至2006年的交通事故死亡人数和经济损失进行网络训练和外推预测。计算结果表明,该模型预测和外推精度高,可用于交通事故预测。     

物联网技术在道路交通安全预测中应用研究

研究道路安全预测精确度问题,由于随机波动性较大的样本数据序列预测精度较差,传统的道路交通安全预测灰色预测理论算法难以解决交通拥塞和道路交通不平衡状况。为了解决上述问题,提出了一种基于物联网技术的交通状态监测安全预测方法,主要采用物联网技术实时监测道路的交通情况,依据搜集的交通信息设计安全预警指标,建立灰色理论道路交通安全预测模型,并在模型的基础上引入二维马尔科夫链时空模型,建立一种新的二维马尔科夫理论的灰色扩展交通安全预测模型。仿真结果表明,提出的新的预测方法能够有效地预测道路交通安全风险以及隐患,预测的精确度要比传统的灰色理论预测模型更高,为交通安全系统设计提供了依据。     

A holonic architecture for the global road transportation system

In recent years the development of automated traffic systems has been gaining increasing attention, and a substantial amount of effort has been invested in trying to find a solution to problems associated to road transport. Among these problems are road accidents caused by human-related factors, such as tiredness, loss of control, a slow reaction time, limited field of view, etc. Another transport-related problem is that of loss of time which may be caused by a slow driving speed due to weather conditions, road conditions, visibility, traffic congestion, etc. In this paper, we present a distributed architecture and the underline distributed algorithm for solving the global road transportation system, which is being developed by several European Universities.     

城市复杂道路网络交通流的分解模型

针对城市道路交通流非线性、不确定性和模糊性特点,将城市道路与快速干道作为整体对待,提出了面向控制应用的城市交通网络宏观动态离散模型。将城市街区作为划分基点,把整个城市道路复杂交通网络分解为交叉口和单向环形道路两个子系统,分别建立了它们的宏观动态模型。通过对交叉口进行理想虚拟变形,将各个单向环形道路连接在一起,从而形成各种复杂网络。对西安市中心区域的实际交通流数据进行了仿真研究,结果表明该交通流模型基本实现了城市道路与快速干道的统一分析建模,较好地反映了城市路网的交通流信息,可以作为城市交通控制系统分析和设计的有力工具。     

A model to estimate and interpret the energy-efficiency of movement patterns in urban road traffic

Urban road traffic is highly dynamic. Traffic conditions vary in time and with location and so do the movement patterns of individual road users. In this article, a movement pattern is the behaviour of a car when traversing a road link in an urban road network. A movement pattern can be recorded with a global navigation satellite system (GNSS), such as the Global Positioning System (GPS). A movement pattern has a specific energy-efficiency, which is a measure of how fuel-intensively the car is moving. For example, a car driving uniformly at medium speed consumes little fuel and, therefore, is energy-efficient, whereas stop-and-go driving consumes much fuel and is energy-inefficient. In this article we introduce a model to estimate the energy-efficiency of movement patterns in urban road traffic from GNSS data. First, we derived statistical features about the car's movement along the road. Then, we compared these to fuel consumption data from the car's controller area network (CAN) bus, normalized to the car's overall range of fuel consumption. We identified the optimal feature set for prediction. With the optimal feature set we trained, tested and verified a model to estimate energy-efficiency, with the fuel consumption serving as ground truth. Existing fuel consumption models usually view movement as a snapshot. Thus, the behaviour of the car remains unknown that causes a movement pattern to be energy-efficient or energy-inefficient. Our model views movement as a process and allows to interpret this process. A movement pattern can, for example, be energy-inefficient because the car is driving in stop-and-go traffic, because it is travelling at high speed, or because it is accelerating. Our model allows to distinguish between these different types of behaviours. Thus, it can provide new insights into the dynamics of urban road traffic and its energy-efficiency.