基于X3D和GPS的立交区域车辆监控框架

针对立交区域的监控系统难以准确实时地对车辆进行监控的问题,该文以X3D和XML为基础,提出在真三维环境中进行车辆监控的技术框架。框架集成三维建模技术、X3D技术、差分GPS技术、数据库技术和车辆运动模型,对行驶在立交区域的车辆进行实时监控,准确定位当前车辆的具体路段。实验证明框架是可行的,可用于车辆监控及交通仿真和车辆行为的分析模拟。     

烟草GIS 车辆监控系统

结合烟草业务发展要求和卷烟物流配送的特点,以建设先进的GIS车辆监控系统,实现综合调度、智能配送、统一管理为目的。本文针对烟草GIS车辆监控系统的特点和需求进行研究,在分析烟草GIS车辆监控系统的功能需求,烟草GIS车辆监控系统技术要求,烟草GIS车辆监控系统具体模块需求的基础上,结合先进的技术和行业实践经验,构建了一个烟草行业GIS车辆监控系统模型,并应用于实际应用的信息系统的开发和实施。     

物流车辆监控系统建模与仿真

物流的运输系统常常需要考虑交通堵塞、路径选择、能源危机、成本控制等各种复杂因素,对物流车辆调度人员提出了很高的要求,因此该文提出建立物流车辆监控仿真系统来模拟物流运输过程中对车辆实时动态的调度。该文结合GIS／GPS／GSM车辆监控系统的工作原理,设计了物流车辆监控系统模型,并构建了基于HLA（High Level Architecture）的物流车辆监控仿真系统框架。从应用的角度分析了HLA仿真开发过程,初步实现物流车辆监控模拟系统联邦,为车辆调度员进行教学和培训提供了一个模拟现实的环境,对物流车辆监控系统的实现起到了一定的辅助作用。     

基于模糊视觉技术的车辆监控系统设计

当前的车辆监测系统进行大型车辆监控的过程中,容易受到环境不可控因素的影响,造成监控过程形式单一,对车辆细节识别准确度较低;提出基于模糊视觉技术的大型车辆监控系统设计方法;系统由硬件和软件这两部分构成;硬件部分以FPGA为控制核心进行了设计,重点对交通图像的采集模块、缓存模块、摄像机方案、监视器方案和嵌入式处理器方案进行阐述;软件部分首先对图像进行预处理消除图像中的噪声.引入车辆细节模糊视觉特征识别模型表示外界随机因素的干扰,根据模型的输出结果计算车辆细节特征的像素密度,能够对车辆的细节状态进行准确识别;实验结果表明,利用设计的监控系统对大型车辆进行监控,能够有效提高监控的准确率,具有较强的稳定性。     

深度神经网络在多场景车辆属性识别中的研究

单一的车辆属性识别已无法满足现有的交通系统,为了提高在实际监控中车辆检测定位的可靠性,利用深度神经网络的思想建立了一种能够在近景监控场景和交通监控场景两种不同场景下识别车辆属性的模型,主要包括车辆类型和颜色两种属性类别。以YOLOv3神经网络为基础,对其进行改进,降低网络深度的同时保证准确率,将车辆类型和颜色属性进行分级训练,提高模型检测速度,此外,创建了AttributesCars车辆属性数据集完成数据准备工作。实验结果表明,所提方法在平均准确率为95.63%的前提下可以满足视频的实时性要求,并且在两种不同场景下均取得了不错的成绩,适用于多场景车辆属性识别。     

ITS和GPS系统下车辆实时调度问题的研究

ITS和GPS技术的发展使得道路交通状况和车辆位置能够被实时监控,从而车辆调度中心能够根据道路交通状况实时调整车辆行驶路线,以避开交通拥挤路段,降低车辆的行驶时间。建立了ITS和GPS系统下车辆实时调度模型,并且就车辆行驶过程中如何降低问题的状态空间进行了研究。     

基于视频监控的违章停车检测算法

针对传统违章停车人工检测方式准确率低、成本高等缺点,提出一种基于视频监控的检测算法.利用码本模型对背景进行建模,用以检测监控场景中的运动目标,通过计算联通区域面积来滤除其他物体对监控车辆的干扰,并根据车辆停留时间将车辆分类,一旦发现违停车辆,系统会发出报警.实验结果表明,该算法准确率高、实时性好.     

车辆监控系统中的多粒度RBAC改进模型

针对传统的RBAC(Role-Based Access Control)模型无法灵活方便地用于车辆监控系统中的权限控制问题,提出一种多粒度RBAC模型的改进模型。引入角色分组和用户分组的概念,并允许同时对角色和用户授权,以及通过车主等身份确认权限等。对于可能出现的权限冲突,通过权限叠加算法加以处理。实验结果表明,该模型在大规模的车辆监控系统中,可以准确高效地完成权限检查。     

基于道路空隙状态的交叉路口违章车辆检测

在智能交通系统中,针对交叉路口当车流量非常大、车辆之间间距太小,不能对背景差分提取的车辆实时地分割这种情况,提出了一种新的基于道路空隙检测的运动车辆监控方法。基于光流场计算估计出背景,利用分区计算实现了对路面信息的简化,基于有限状态机的模型根据道路空隙状态的迁移判断出车辆的越线超车行为。研究结果表明：此方法能实时监控交叉路口复杂情况时的车辆行为,同时能很好地解决光照变化、噪声干扰情况下的车辆行为监控。     

电动汽车远程监控与服务系统开发

针对电动汽车设计了远程监控与服务系统。该系统基于数据采集和无线远程数据传输,实现了车辆运行参数的实时上传与存储。车载控制器完成数据采集与发送,服务器按照模型对象对数据进行存储、分析与处理,客户端为用户提供监控和命令视图,实现车辆的监控功能。服务器通过对运行数据进行分析处理获取车辆服务信息并发送给车载终端控制器,实现车辆的远程服务功能。该系统在长城A0级小型城市纯电动轿车试验样车上测试,具有良好的实用性及稳定性。     

一种新的一维无格子超车跟车仿真模型

近年来,人们对超车和跟车进行了广泛的研究。但以往方法尚有其不足之处。主要体现在,不能统一处理超车和跟车模型以及不能较好地体现司机的行为特性,另外不能通过简洁的计算同时给出任意时刻车辆在道路的纵向和横向上的位置。为此,作者在其首创的固支梁挠度曲线超车跟车模型基础上,提出了一种新的一维无格子超车跟车模型。它根据前方车辆的情况及道路宽度的变化情况直接确定当前车辆在道路宽度方向上的位置。新模型能同时适用于道路宽度变化和前方车辆速度变化的情形。     

AUV载荷分离方法及安全性研究

自主式水下航行器(AUV)可视为载荷和运载体组成的一个多体系统,在载荷释放时必须保证分离后载荷不能与运载体发生碰撞.在对AUV分离运动数学模型研究的基础上,推导了载荷和运载体初始位置和适于攻角的计算模型,并给出了推冲分离方法和重力加小推冲分离方法的碰撞模型.在此基础上对两种分离方法下载荷的运动进行了仿真和分析,通过仿真结果可知,从安全性考虑重力加小推冲分离方法更适合载荷的水下分离运动,而且具有很高的工程实用价值.     

An observer-based approach for the projection onto a 2d-curve under movement

Standard path control laws of autonomous vehicles use the shortest distance between the vehicle’s position and the path as a control error. In order to determine this distance, the projection point onto the path needs to be determined continuously. This requires fast algorithms that feature high numerical reliability in the field of vehicle application.This paper presents two different observer-based approaches for the projection problem. The identity observer reconstructs all states of interest for path control. The second one, a reduced observer, only possesses the curve parameter as a state and calculates the other values by algebraic formulas. Both algorithms consider the continuous movement of the vehicle, the run of the curve, and work without any approximation of the curve. Furthermore, they are applicable for arbitrary parameterized smooth curves, guarantee the required numerical stability, have short calculating time, and show good statistical properties. The performance is shown in several simulations as well as under real conditions.     

一种基于GPS与GIS集成的车辆追踪系统解决方案

本文提出了一种集成GPS与GIS技术的车辆追踪系统的设计方案与实现机制，分析了系统结构，并讨论了盲区问题的解决方法。     

A computer aided design method for car form and its application based on shape parameters

In the early design stage, automotive modeling should both meet the requirements of aesthetics and engineering. Therefore, a vehicle CAD (computer aided design) model that can be easily adjusted by feedbacks is necessary. Based on CE-Bézier surface, this paper presents a set of algorithms for parametric segmentation and fairing surface generation in a car model. This model is defined by a simplified automotive template and relevant control points, shape parameters and segmentation parameters, which can be modified to alter the car form efficiently. With this model and the corresponding adjustment method, more than fifty various vehicle models are established in this research according to different parameters. And two methods for calculating similarity index between car models are constructed, which are suitable for brand design trend analysis and modelling design decisionmaking.     

A hierarchical simple particle swarm optimization with mean dimensional information

To reduce the negative influence of the overemphasis of gbest the dimensional information of particle is introduced to be a new example. This additional information source is incorporated into simple PSO to establish a simpler position model. Another two simpler position updating models, cognition only model and social only model, based on the simple PSO algorithm are presented as well. Time hierarchy strategy is extended from probability hierarchy, both aiming to make full use of advantages of three models. Three models are used with time or probability hierarchy to update each particle’s position. Thus, two proposed algorithms THSPSO and PHSPSO are finally obtained. Experiments are conducted on fifteen benchmark functions. The results demonstrate the two proposed algorithms both have excellent performances for basic functions compared with other popular PSO variants. Probability hierarchy strategy is more effective than time hierarchy strategy in general.     

Identification of multibody vehicle models for crash analysis using an optimization methodology

This work proposes an optimization methodology for the identification of realistic multibody vehicle models, based on the plastic hinge approach, for crash analysis. The identification of the design variables and the objective function and constraints are of extreme importance for the success of the optimization. The characteristics of the plastic hinges are used as design variables while the objective functions are formulated with measures of the difference between the dynamic response of the model and a reference response. The sequential application of genetic and gradient-based optimization methods is used to solve the optimization problem constituting a systematic approach to the automatic identification of vehicle multibody models. The methodology is demonstrated with the identification of the multibody model of a large family car for side and front crash. The vehicle model is developed in the MADYMO multibody code which is linked with the optimization algorithms implemented in the Matlab Optimization Toolbox.     

Dynamic Traffic Assignment with More Flexible Modelling within Links

Traffic network models tend to become very large even for medium-size static assignment problems. Adding a time dimension, together with time-varying flows and travel times within links and queues, greatly increases the scale and complexity of the problem. In view of this, to retain tractability in dynamic traffic assignment (DTA) formulations, especially in mathematical programming formulations, additional assumptions are normally introduced. In particular, the time varying flows and travel times within links are formulated as so-called whole-link models. We consider the most commonly used of these whole-link models and some of their limitations.In current whole-link travel-time models, a vehicle's travel time on a link is treated as a function only of the number of vehicles on the link at the moment the vehicle enters. We first relax this by letting a vehicle's travel time depend on the inflow rate when it enters and the outflow rate when it exits. We further relax the dynamic assignment formulation by stating it as a bi-level program, consisting of a network model and a set of link travel time sub-models, one for each link. The former (the network model) takes the link travel times as bounded and assigns flows to links and routes. The latter (the set of link models) does the reverse, that is, takes link inflows as given and finds bounds on link travel times. We solve this combined model by iterating between the network model and link sub-models until a consistent solution is found. This decomposition allows a much wider range of link flow or travel time models to be used. In particular, the link travel time models need not be whole-link models and can be detailed models of flow, speed and density varying along the link. In our numerical examples, algorithms designed to solve this bi-level program converged quickly, but much remains to be done in exploring this approach further. The algorithms for solving the bi-level formulation may be interpreted as traveller learning behaviour, hence as a day-to-day traffic dynamics. Thus, even though in our experiments the algorithms always converged, their behaviour is still of interest even if they cycled rather than converged. Directions for further research are noted. The bi-level model can be extended to handle issues and features similar to those addressed by other DTA models.     

空间再入飞行体多传感观测融合仿真研究

空间飞行器再入飞行的可靠观测无疑是一个值得关注和探讨的问题。弹道导弹是一种典型的空间再入飞行器,其沿着预定弹道飞行,速度快,且可发生机动。对于弹道导弹的运动可以用较为简单和基本的运动模型的交互来近似,也可以用地面雷达和光学传感器来观测。将近似运动模型和观测值代入专门针对非线性估计问题的无味卡尔曼滤波(UKF)框架中,再利用数据融合算法,可以实现弹道导弹的航迹估计。文中首先描述了弹道导弹的运动模型,继而给出了不同传感器的量测方程和运动体的基本运动模型,然后阐述了交互多模型(IMM)、UKF及多传感数据融合算法,最后在仿真系统中实现航迹并得出结论。     

A fusion strategy for reliable vehicle positioning utilizing RFID and in-vehicle sensors

In recent years, RFID has become a viable solution to provide object's location information. However, the RFID-based positioning algorithms in the literature have disadvantages such as low accuracy, low output frequency and the lack of speed or attitude information. To overcome these problems, this paper proposes a RFID/in-vehicle sensors fusion strategy for vehicle positioning in completely GPS-denied environments such as tunnels. The low-cost in-vehicle sensors including electronic compass and wheel speed sensors are introduced to be fused with RFID. The strategy adopts a two-step approach, i.e., the calculation of the distances between the RFID tags and the reader, and then the global fusion estimation of vehicle position. First, a Least Square Support Vector Machine (LSSVM) algorithm is developed to obtain the distances. Further, a novel LSSVM Multiple Model (LMM) algorithm is designed to fuse the data obtained from RFID and in-vehicle sensors. Contrarily to other multiple model algorithms, the LMM is more suitable for current driving conditions because the model probabilities can be calculated according to the operating state of the vehicle by using the LSSVM decision model. Finally, the proposed strategy is evaluated through experiments. The results validate the feasibility and effectiveness of the proposed strategy.