基于Android的动态称重数据检测系统

设计了一种基于Android操作系统的动态称重数据检测系统,通过该检测系统可以获取行驶车辆的动态称重实时数据和历史数据。该系统利用动态称重传感器的数据记录器作为服务器端,在Android客户端程序中访问该数据记录器,通过与其建立WebSocket连接获取实时数据,通过REST风格的HTTP请求获取历史数据,设计完成了动态称重数据的Android检测系统。实验结果表明,基于Android的动态称重数据检测系统具有便捷、成本低等特点,满足车辆动态称重的应用要求。     

基于动态权值的多分类器故障诊断系统

 为提高动态系统故障诊断的精确性,以及减少系统运行环境对故障诊断带来的影响,本文提出了一种基于动态权值的多分类器故障诊断系统.该方法使用决策支持度来衡量当前诊断任务中各分类器的实时决策可信度,并将其联合分类器性能指标动态地为各分类器赋予融合权值,决策性能好且决策支持度高的分类器决策结果获得较大的融合权值,同时,使不可靠决策结果的融合权值趋近于零.在此基础上,将多分类器系统优化为实时性能较好的分类器组成的子系统进行故障诊断,减少了不可靠决策的干扰,进一步提高了融合决策的精确度.试验表明本文方法具有良好的诊断决策性能,能获得比单个分类器和常用的一些融合算法更高的分类准确度.     

超限检测系统常见故障及处理措施

超限检测系统是高速公路中最重要的管理车辆是否超载的评定器材,其工作效率和状态直接决定了高速公路通车的情况和运输业的运行情况。超限检测系统由称重系统和抓拍系统构成。为降低故障发生率,根据不同检测点的故障情况,给出相对于故障情况出现机率最多的几种处理措施,为使用单位和设备厂家提高工作效率。     

基于车型特征提取的车辆检测程序的实现

车辆检测系统是智能交通系统的重要环节,基于视频图像的车型识别和车辆检测技术研究也越来越多。本文以实时视频为基础,介绍了一种车辆特征提取算法-弹性松弛袋算法,来提取车辆的车长车高特征,从而实现对车辆的检测和分类。本文着重介绍了弹性松弛袋算法提取车型特征的程序实现,通过实验的结果,验证了该算法的可行性。     

基于纹理特征的汽车车型识别

提出了一种基于纹理特征的汽车车脸车型识别方法。首先对车辆图像进行预处理,再通过基于灰度共生矩阵的纹理分析,得到纹理特征值,即待识别车型的纹理特征,最后利用最小距离分类器进行车型识别。实验结果表明,文中提出的车型识别方法简便、快捷、有效。     

一种面向“互联网+”智能交通管理的车辆类型识别方法

针对现有智能交通管理中车辆类型识别方法存在分类器效率较低等诸多问题,通过构造一种新的分类器,建立了一种智能交通车辆类型识别新方法。首先采用边缘梯度直方图进行图像特征提取,然后通过融合纠错编码技术和K-近邻分类器构造新分类器实现车辆类型的分类。通过大量实验仿真分析比对表明,该方法不仅能将多类分类问题转化成多个两分类问题,而且使车辆类型识别效率提高了2%,鲁棒性好。因此,该方法在"互联网+"智能交通管理中具有广阔的应用前景和推广价值。     

基于智能图像处理技术的车型识别

文章提出了一种应用干视频车型识别系统中的图像预处理方法，该方法对采集到的车辆图像处理后可以得到连续、光滑的边缘图像，与构建的车型库相结合，可以应用干公路车辆车型的检测与识别中。实验表明，识别过程快速、准确率较高。具有一定的参考价值。     

基于毫米波雷达的露天矿区车辆防撞自动刹车系统

针对防撞自动刹车系统容易受到复杂动态环境影响,导致自动刹车距离与实际情况不符的问题,设计基于毫米波雷达的露天矿区车辆防撞自动刹车系统。添加毫米波雷达传感器,通过集成显示器,为车辆行驶提供高清画面。使用360度全景影像监控,实时采集周围高清视频画面。引入无量纲量和中间比较因子,计算行车间距。通过图像动态识别算法,实时显示清晰路况,精准确定目标位置。通过实验得出,该系统自动刹车曲线与实际刹车曲线基本相符,最大横向误差只有2 m。     

基于DSP的动态称重系统设计

通过分析目前国内外使用的动态称重系统（WIM）存在的问题,选择石英压电称重传感器作为动态称重传感器,并采用TMS320C6416的DSP核心器件设计并实现了相应的动态称重系统,利用Delphi完成了该动态称重系统的软件设计。该系统克服了传统动态称重系统测量精度低、所需时间长等缺点,通过实测表明该系统运行良好,应用前景广泛。     

基于STM32与GPRS的车载称重系统研究

文章针对目前的车辆称重系统和货车源头治超的现状,介绍了一种基于STM32F107VCT6处理器和GPRS网络的车载称重解决方案,通过GPS装置对车辆位置进行实时定位,利用GPRS网络将车辆的载荷量、位置、速度等车辆信息发送至车辆监控中心。进行了车辆水平静态和动态加载称重试验,经过了径向基神经网络(RBF网络)的验证,并验证了不同环境下GPRS的收发情况。试验结果表明,系统称重精度高,GPS定位信息准,GPRS收发稳定,实用性强,在各种情况下都能够满足当前的车载称重需求。     

Experience with an On‐board Weighing System Solution for Heavy Vehicles

Mining, construction, and other special vehicles for heavy use are designed to work under high‐performance and off‐road working conditions. The driving and executive mechanisms of the support structures and superstructures of these vehicles frequently operate under high loads. Such high loads place the equipment under constant risk of an accident and can jeopardize the dynamic stability of the machinery. An experimental investigation was conducted on a refuse collection vehicle. The aim of this research was to determine the working conditions of a real vehicle: the kinematics of the waste container, that is, a hydraulic rotate drum for waste collection; the dynamics of the load manipulator (superstructure); the vibrations of the vehicle mass; and the strain (stress) of the elements responsible for the supporting structure. For an examination of the force (weight) on the rear axle of a heavy vehicle, caused by its own weight and additional load, a universal measurement system is proposed. As a result of this investigation, we propose an alternative system for continuous vehicle weighing during waste collection while in motion, that is, an on‐board weighing system, and provide suggestions for measuring equipment designs.     

Vehicle detection and inter-vehicle distance estimation using single-lens video camera on urban/suburb roads

This paper presents a driver assistance system for vehicle detection and inter-vehicle distance estimation using a single-lens video camera on urban/suburb roads. The task of vehicle detection on urban/suburb roads is more challenging due to their high scene complexity. In this work, the still area of frame inside the host vehicle is first removed using temporal differencing, followed by detecting vanishing point. Segmentation of road regions is then conducted using vanishing point and road’s edge lines. Shadow regions at the bottoms of vehicles verified using the HOG feature and an SVM classifier are utilized to detect vehicle positions. The distances between the host and its front vehicles are estimated based on the locations of detected vehicles and vanishing point. Experimental results show varied performance of vehicle detection with different scenes of urban/suburb roads and the detection rate can achieve up to 94.08%, indicating the feasibility of the proposed method.     

基于MATLAB的高速公路车辆违章占道分析及预警系统

系统通过MATLAB的图像处理功能对摄像头拍摄的视频图像进行处理,采用基于面积检测的背景差分法识别车辆位置,运用霍夫变化和卡尔曼滤波处理得到图像中路段的车道线,进而判断车辆所处车道.运用帧间差分法准确测出车辆速度,结合高速公路行车规定,即可判断出车辆是否处于违规占道行驶状态.系统可以识别违规车辆的车牌号,结合设定的LE...     

一种动态寻路中的机会转发模型

动态寻路（Dynamic Routing）是一项用以解决城市交通拥堵的智能交通技术.通过让一些车辆产生和转发路况拥堵消息,另一些车辆能够避开拥堵路段,从而缓解交通状况.以“存储-携带-转发”为通信模式的机会网络被广泛运用于动态寻路研究.文中提出了一种新型路况信息的机会转发算法,算法综合考虑车辆的位置和行驶方向.仿真实验表明,算法在保持信息散布效果的前提下,显著降低了网络负载,提升了效率.     

Automatic Moving Vehicle Detection and Classification Based on Artificial Neural Fuzzy Inference System

The main objective of this work is to automatically detect moving vehicles on the road. Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier is adopted in this paper to classify moving vehicles on road. An input traffic video scenes are taken with vertical and horizontal positioned cameras. The proposed system contains six major steps such as preprocessing, vehicle detection, tracking, structural matching, feature extraction and classification. In this proposed method, preprocessing consists of color conversion and noise removal. Vehicle detection is performed by using background subtraction and Otsu thresholding algorithm. Kalman filter is used in the third step to track moving vehicles in successive frames. In the fourth step, Active Shape Modelling method is used to recover the 3D shape of the vehicle in order to find the boundaries of vehicle. In the fifth step, features of the detected vehicles are extracted by Harrish corner detector, log Gabor filter and these features are taken into account to classify the types of vehicle. Finally, ANFIS is proposed to classify the vehicles which is trained by updating the membership function. Experimentation results provides better accuracy rate and low mean error rate when compared with the state of art methods.     

A real-time detector for parked vehicles based on hybrid background modeling

In this paper, a real-time detection system based on hybrid background modeling is proposed for detecting parked vehicles along the side of a road. The hybrid background model consists of three components: (1) a scene background model, (2) a computed restricted area map, and (3) a dynamic threshold curve for vehicles. By exploiting the motion information of normal activity in the scene, we propose a hybrid background model that determines the location of the road, estimates the roadside and generates the adaptive threshold of the vehicle size. The system triggers a notification when a large vehicle-like foreground object has been stationary for more than a pre-set number of video frames (or time). The proposed method is tested on the AVSS 2007 PV dataset. The results are satisfactory compared to other state-of-the-art methods.     

基于卡尔曼滤波算法的城市交叉路口车辆检测及分类

为了向相关部门提供更多的过往车辆信息以满足道路交通的需求,设计了一个基于卡尔曼滤波算法的城市交叉路口车辆检测及分类系统,用于对过往的车辆进行检测、计数和分类.首先采用背景差分法和卡尔曼滤波算法对在检测区的车辆进行检测和跟踪;然后使用经过检测、处理的被测车辆图像触发距其最近的相机进行图形分割;最后,通过LDA分类器对分段车辆的几何形状及外观特征进行正确地分类.所提系统的有效性在摄取的3400帧视频序列上得到了验证,实验结果表明,系统的检测率可达97.44％,正确分类率可达88.0％,与先进的方法相比,取得了更好的检测性能.     

Vehicle detection using normalized color and edge map.

This paper presents a novel vehicle detection approach for detecting vehicles from static images using color and edges. Different from traditional methods, which use motion features to detect vehicles, this method introduces a new color transform model to find important "vehicle color" for quickly locating possible vehicle candidates. Since vehicles have various colors under different weather and lighting conditions, seldom works were proposed for the detection of vehicles using colors. The proposed new color transform model has excellent capabilities to identify vehicle pixels from background, even though the pixels are lighted under varying illuminations. After finding possible vehicle candidates, three important features, including corners, edge maps, and coefficients of wavelet transforms, are used for constructing a cascade multichannel classifier. According to this classifier, an effective scanning can be performed to verify all possible candidates quickly. The scanning process can be quickly achieved because most background pixels are eliminated in advance by the color feature. Experimental results show that the integration of global color features and local edge features is powerful in the detection of vehicles. The average accuracy rate of vehicle detection is 94.9%.     

基于路径时延模型的车联网数据分发方案

针对车联网的容迟特性造成通信资源受限的问题,提出了满足副本抑制要求的数据分发方案.方案利用马尔可夫链,通过交通网络的车辆概率分布建立路段的期望传输时延,并结合车辆的轨迹与目标位置的匹配度确定车辆的转发优先级.车辆为转发的每个数据包插入转发参数字段并通过同步反馈机制确定最终的转发车辆,确保由优先级最高的车辆完成转发.考虑到链路的稳定性,还推导了当前丢包率前提下,车辆接收数据包与发送次数之比,避免不必要的发送尝试产生大量副本.实验结果显示,提出的方案与基于轨迹预测的算法相比,有效提高了网络吞吐量和时延性能.