反导预警探测系统作战效能灰色模糊评估

针对评价指标的模糊性和评价信息的不确定性,运用灰色模糊理论探索反导预警探测系统作战效能评估问题。在分析系统作战效能影响因素的基础上,构建了对其进行评估的指标体系,应用灰色模糊数学理论,将隶属度和灰度综合到评估过程中,给出系统作战效能的评估模型。该方法能够有效结合定性与定量分析,为反导预警探测系统作战效能的评估提供了一种较为有效的方法和途径。     

Road curvature estimation for vehicle lane departure detection using a robust Takagi–Sugeno fuzzy observer

In this paper, a lane departure detection method is studied and evaluated via a professional vehicle dynamics software. Based on a robust fuzzy observer designed with unmeasurable premise variables with unknown inputs, the road curvature is estimated and compared with the vehicle trajectory curvature. The difference between the two curvatures is used by the proposed algorithm as the first driving risk indicator. To reduce false alarms and take into account the driver corrections, a second driving risk indicator is considered, which is based on the steering dynamics, and it gives the time to the lane keeping. The used nonlinear model deduced from the vehicle lateral dynamics and a vision system is represented by an uncertain Takagi–Sugeno fuzzy model. Taking into account the unmeasured variables, an unknown input fuzzy observer is then proposed. Synthesis conditions of the proposed fuzzy observer are formulated in terms of linear matrix inequalities using Lyapunov method. The proposed approach is evaluated under different driving scenarios using a software simulator. Simulation results show good efficiency of the proposed method.     

一种提高警戒雷达目标检测能力的数据处理方法

提出了一种提高警戒雷达目标检测能力的数据处理方法——溯相关检测法。该法基于雷达帧间数据积累方式，利用目标的航迹相关特性降低噪声虚警，提高警戒雷达的信号检测能力和探测距离，模拟结果表明在满足虚警率为10-E6的要求下，当输入信噪比为3db时，其发现概率可由原来的0．5提高到0．9，从而大大提高了雷达的目标检测性能。     

铁路人身安全防护现状分析与关键对策研究

有效保障铁路施工人员的人身安全,是国内铁路建设与运营亟需解决的问题之一。概述国内外铁路人身安全形势,分析人工防护管理体系与既有各类技术防护方法的弊端,从列车定位、人员定位、综合通信与预警模型构建等4个方面详细研究关键对策。     

浅谈海外工程施工承包项目的本土化管理

结合柬埔寨项目前期管理实践,从海外工程施工承包项目的本土化管理在本海外项目的运用及原因分析、如何进行本土化管理以及对以后管理工作的启示这三个方面进行了论述,有关的尝试和探索可为同行提供一定的参考。     

铁路专业气象服务系统的设计与实现

通过对铁路防洪抗汛业务及现状的分析,规划了铁路专业气象服务系统的目标和主要功能,研究并提出了系统结构、应用模式、网络方案和数据处理流程等设计思想和解决方案,论述了系统设计采用的关键技术,并说明了系统的特点.     

An on-road evaluation of connected motorcycle crash warning interface with different motorcycle types

Crash warning systems have been deployed in the high-end vehicle market segment for some time and are trickling down to additional motor vehicle industry segments each year. The motorcycle segment, however, has no deployed crash warning system to date. With the active development of next generation crash warning systems based on connected vehicle technologies, this study explored possible interface designs for motorcycle crash warning systems and evaluated their rider acceptance and effectiveness in a connected vehicle context. Four prototype warning interface displays covering three warning mode alternatives (auditory, visual, and haptic) were designed and developed for motorcycles. They were tested on-road with three connected vehicle safety applications - intersection movement assist, forward collision warning, and lane departure warning - which were selected according to the most impactful crash types identified for motorcycles. Combined auditory and haptic displays showed considerable promise for implementation. Auditory display is easily implemented given the adoption rate of in-helmet auditory systems. Its weakness of presenting directional information in this study may be remedied by using simple speech or with the help of haptic design, which performed well at providing such information and was also found to be attractive to riders. The findings revealed both opportunities and challenges of visual displays for motorcycle crash warning systems. More importantly, differences among riders of three major motorcycle types (cruiser, sport, and touring) in terms of rider acceptance of a motorcycle crash warning system were revealed. Based on the results, recommendations were provided for an appropriate crash warning interface design for motorcycles and riders in a connected vehicle environment.     

Study on the framework of hybrid collision warning system using loop detectors and vehicle information

Safety warning systems generally operate based on information from sensors attached to individual vehicles. Various types of data used for collision risk calculation can be categorized into two types, microscopic or macroscopic, depending on how the sensors collect the information of traffic state. Most collision warning systems use only either of these types of data, but they all have limitations imposed by the data, such as requirement of high installation cost and high market penetration rate of devices. In order to overcome these limits, we propose a collision warning system that utilizes the integrated information of macroscopic data and microscopic data, from loop detectors and smartphones respectively. The proposed system is evaluated by simulating a real vehicle trip based on the NGSIM data. We compare the results against collision warning systems based on macroscopic data from infrastructure and microscopic data from Vehicle-to-Vehicle information. The analysis of three systems shows two findings that (a) ICWS (Infrastructure-based Collision Warning System) is inadequate for immediate collision warning system and (b) VCWS (V2V communication based Collision Warning System) and HCWS (Hybrid Collision Warning System) produce collision warning at very similar timing, even with different behavior of individual drivers. Advantages of HCWS are that it can be directly applied to existing system with small additional cost, because data of loop detector are already available to be used in Korea and smartphones are widely spread. Also, the computation power distributed to each individual smartphone greatly increases the efficiency of the system by distributing the computation resources and load.     

Utilizing naturalistic driving data for in-depth analysis of driver lane-keeping behavior in rain: Non-parametric MARS and parametric logistic regression modeling approaches

It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior.     

快速公交(BRT)专用道沥青路面早期损害分析

BRT在我国的应用越来越广泛,我国BRT专用道路面大部分采用沥青路面。在建设使用早期,都出现了不同程度的早期破坏。这不仅对社会、交通造成了交通影响,也在经济上造成了很大的损失。因此,BRT专用道路基路面的早期损坏问题引起了广泛的关注。该文针对BRT专用道沥青路面出现的早期破坏进行分析,积累经验,为其他的工程提供参考。