基于逆透视变换的车辆排队长度检测方法

交通路口的车辆排队长度检测是智能交通系统的重要组成部分,传统的检测方法易受背景噪声、摄像机透视效果等因素的干扰造成检测失败,而且其实现都是基于串行结构的处理器,不能用于实时处理的场合。本文设计了一种充分利用平直道路几何特征并适合FPGA实现的排队长度自动检测算法,该算法利用逆透视变换消除图像几何失真,引入公路的结构性约束有效检测了车道线;接着采用Sobel边缘算子检测出各车道的车辆轮廓,通过一种基于信息量的度量方法提取排队的队尾,从而确定了车辆排队长度,并且通过硬件化设计使得整个检测过程达到实时的处理速度。     

单交叉路口交通灯实时配时算法的研究

研究了城市道路交通信号实时控制系统的问题,重点论述了单交叉路口交通信号灯控制(点控制)模型的实时配时算法.根据城市交通流分布规律,设计出流量序列生成算法来模拟实时交通流;通过建立信号灯动态模型从而获得最佳周期长度和有效绿灯时间,然后采用模糊控制算法对信号灯配时方案进行实时的动态优化调整,并建立基于排队的车辆延误模型来对模糊控制算法进行评价.计算机模拟仿真的结果表明所设计实时配时方案比定时配时方案有显著的改善.     

基于角点特征分析的车辆排队长度检测方法

针对交通堵塞造成的各种状况,通过视频分析实现实时高效的车辆排队长度检测,从而获取更多的交通信息改善交通状况.本文通过传统的FAST角点检测方法与运动检测的过程相结合得到改进后的FAST算法,使用改进后的FAST角点特征分析技术,不仅可以提取出当前交通道路上表征车辆存在的角点特征图,还可以获取角点位置的运动状态.通过对交通监控下的视频进行预处理后,单一车道内处于静态的角点特征形成车辆排队,并进行PCA处理得到一维向量,最后对一维向量进行形态学处理来检测单一车道内的车辆排队长度.实验表明,本方法检测精度平均98%,满足应用于实际场景.     

交通信号灯智能控制算法研究

针对当前城市交通信号灯控制的技术缺陷,即国内大多数城市交通信号灯控制方法仍然停留在时间程序控制的技术层面,或者采用感应线圈等设备来获取道路交通信息等技术现状,提出一种交通信号灯智能控制算法。该系统算法由图像边缘检测、道路行车类型切割、纵向坐标投影和车流长度分析等核心技术组成,能够充分、有效和实时地获取交通流量信息,使城市交通信号灯的开／关时间能够根据道路上车流量的实际大小实施准确控制,因此使交通信号灯的控制达到智能化的技术水平,为最终实现城市智能交通提供科学的信息基础。     

Real-time road traffic state prediction based on ARIMA and Kalman filter

The realization of road traffic prediction not only provides real-time and effective information for travelers, but also helps them select the optimal route to reduce travel time. Road traffic prediction offers traffic guidance for travelers and relieves traffic jams. In this paper, a real-time road traffic state prediction based on autoregressive integrated moving average (ARIMA) and the Kalman filter is proposed. First, an ARIMA model of road traffic data in a time series is built on the basis of historical road traffic data. Second, this ARIMA model is combined with the Kalman filter to construct a road traffic state prediction algorithm, which can acquire the state, measurement, and updating equations of the Kalman filter. Third, the optimal parameters of the algorithm are discussed on the basis of historical road traffic data. Finally, four road segments in Beijing are adopted for case studies. Experimental results show that the real-time road traffic state prediction based on ARIMA and the Kalman filter is feasible and can achieve high accuracy.     

Adaptive Road Recognition and Ego-state Tracking in the Presence of Obstacles

An approach to road recognition and ego-state tracking in monocular image sequences of traffic scenes is described. The main contribution of this paper is the adaptive recognition scheme, which deals with competitive road hypotheses, and its application in several processing steps of an image sequence analysis system. No manual initialization of the tracked road is required and the change of the road type is allowed. The road parameters to be recognized are the road width, road lane number and road curvature. For exact estimation of road curvature the translational and rotational velocities of the ego-car are assumed to be available. The estimated ego-state parameters are the camera orientation (which is derived due to vanishing point tracking) and the camera position relative to the road center line.     

基于视频分析的车辆排队长度检测

本文实现了一个完整的车辆排队长度检测算法。检测算法分为两步：车辆排队检测和队列长度计算。对白天和夜晚不同光照条件采用不同的队列检测算法,并实现了白天夜晚检测算法的自动切换。在白天光照条件下,采用移动检测窗来进行车辆排队检测,在每一个检测窗内,通过三帧差法运动检测和形态学边缘检测进行车辆存在检测两步判断有无车辆排队。针对夜晚场景,提出利用车灯这一显著特征进行车辆排队检测。以图像灰度直方图的相关系数作为距离值,利用kNN分类器进行白天夜晚的识别,实现检测算法的自动切换。队列长度计算通过摄像机标定完成,找到一种仅利用车道线的、简单有效的摄像机标定方法。实验表明该方法可以准确检测出车辆排队并计算出其长度。     

面向复杂城市交通场景的一种实时车道线检测方法

车道线检测是智能交通监控及自动驾驶的基础步骤,为提高其鲁棒性和实时性,针对复杂城市交通场景中自动驾驶需要检测车道线的需求,提出了一种实时车道线检测算法,首先运用改进灰度化变换突显车道线的特征,并通过改进的Gabor滤波算法增强车道线的边缘信息;最后采用多约束霍夫变换筛选得到平行车道线从而实现实时车道线检测。实验表明,该方法在三种不同真实的交通道路场景下,提高了车道线检测精度及处理速度,可应用于实时车道线检测系统。     

A new congestion control algorithm for improving the performance of a broadcast-based multiprocessor architecture

Congestion occurring in the input queues of broadcast-based multiprocessor architectures can severely limit their overall performance. The existing congestion control algorithms estimate congestion based on a node’s output channel parameters such as the number of free virtual channels or the number of packets waiting at the channel queue. In this paper, we have proposed a new congestion control algorithm to prevent congestion on broadcast-based multiprocessor architectures with multiple input queues. Our algorithm performs congestion control at the packet level and takes into account the next input queue number which will be accessed by the processor, which form the fundamental differences between our algorithm and the algorithms based on the idea of virtual channel congestion control. The performance of the algorithm is evaluated by OPNET Modeler with various synthetic traffic patterns on a 64-node Simultaneous Optical Multiprocessor Exchange Bus (SOME-Bus) architecture employing the message passing protocol. Performance measures such as average input waiting time, average network response time and average processor utilization have been collected before and after applying the algorithm. The results show that the proposed algorithm is able to decrease the average input waiting time by 13.99% to 20.39%, average network response time by 8.76% to 20.36% and increase average processor utilization by 1.92% to 6.63%. The performance of the algorithm is compared with that of the other congestion control algorithms and it is observed that our algorithm performs better under all traffic patterns. Also, theoretical analysis of the proposed method is carried out by using queuing networks.     

Processor-shared service systems with queue-dependent processors

In this investigation, we analyze the finite queue-dependent heterogeneous multiprocessor service system in which processors are shared by more than one job. Whenever, the queue length of jobs in the system reaches a threshold value N_j(j=1,2,…,r−1), the (j+1)th processor starts the processing of the jobs and continues till queue length is again decreased to the same level. Steady-state queue size distribution is obtained using recursive method considering Markovian arrival and service times. We derive the system characteristics viz. expected number of jobs in the system, throughput of the system, probability that jth (j=2,3,…,r) processor rendering the service etc. A cost relationship is constructed to determine the optimal threshold levels for processors being active in order to gain maximum net profit. For illustration purpose, tables and graphs are also provided.     

Road scene analysis for determination of road traffic density

Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.     

应用图像处理方法自动检测路口车辆排队长度

在智能交通处理系统中，路口车流参数——车辆排队长度，占空比等可以为很多情况提供必要的信息，如交通阻塞及交通事故的监控、交通信号灯的控制等。其中交通路口车流的长度是车流参数中最重要的一个。本文提出了一种基干图像局部特征的路口车辆排队长度的检测方法，通过融合图像的点特征(角点)和线特征(边缘)，完成车流长度的检测，包括停止车流的长度以及在可视范围内整体车流的长度。实验结果表明，这种方法实现简单、应用效果良好，具有较好的应用前景。     

基于UE4的在线驾驶仿真系统设计与实现

驾驶仿真系统在交通安全研究中发挥着重要作用,但目前仍存在三维道路建模受限、仿真场景还原度不高等问题;研究采用虚幻引擎开发仿真功能模块,根据道路几何线形与交通流原始资料,能高效搭建出驾驶仿真场景;采用像素流送技术将视频帧发送到前端,设置硬件设备的轴属性映射,支持模拟驾驶外部信号输入,并实时将驾驶行为数据存入数据库,设计了一种轻量化、自动化、智能化的在线驾驶仿真信息管理与可视化系统;系统主要实现了道路自动化建模、人-车-路-环境信息管理、车道级真实交通流场景还原、在线模拟驾驶与数据可视化等功能;经测试,系统部署后能在Web端稳定运行,弥补了现有驾驶仿真系统的不足,为实验人员提供轻量高效的驾驶仿真服务。     

基于物联网的城市道口机动车智能监控和行为检测系统

物联网架构的城市道口机动车监控和行为分析系统在传统的物联网定义基础上,提出了包含感知／发布层、智能中间层、网络层、服务层和应用层的智能监控架构、以高清网络监控相机为采集终端,使用自行开发的图像分析算法和软件实现了道路拥堵状态判定以及多目标车辆追踪,实时采集视频流中道口监控区域内车辆移动轨迹、速度、前方道路拥堵状态等基本参数;在通信层使用3G／Wi—Fi等高速无线通信技术与有线通信相结合实时传输关键参数和监控图像,应用层使用自行开发的中心监控软件平台实现了对关键数据的存储、统计、业务应用的服务支持和管理,应用层针对电子警察、智能交通监控和信息发布展开,结合交通控制信息和我国交通法规为违章实时判定、交通信息采集等提供了基础、     

基于Spark的实时视频分析系统

视频监控技术在交通管理、公共安全、智慧城市等方面有着广泛的应用前景,且向着智能识别、实时处理、大数据分析的方向发展. 本文针对大规模实时视频监控提出了新的解决方案. 基于Spark streaming流式计算、分布式存储及OLAP框架,使多路视频处理在可扩展性、容错性及数据多维聚合分析上具有明显的优势. 系统根据视频处理算法划分为单机处理与分布式处理. 并将视频图像处理与数据分析耦合,利用Kafka消息队列与Spark streaming完成对多路视频输出数据的进一步操作. 结合分布式存储方案,并利用OLAP框架实现对海量数据实时多维聚合分析与高效实时查询.     

Representation of occurrences for road vehicle traffic

Our 3D-model-based Computer Vision subsystem extracts vehicle trajectories from monocular digitized videos recording road vehicles in inner-city traffic. Steps are documented which import these quantitative geometrical results into a conceptual representation based on a Fuzzy Metric-Temporal Horn Logic (FMTHL, see [K.H. Schäfer, Unscharfe zeitlogische Modellierung von Situationen und Handlungen in Bildfolgenauswertung und Robotik, Dissertation, 1996]). The facts created by this import step can be understood as verb phrases which describe elementary actions of vehicles in image sequences of road traffic scenes. The current contribution suggests a complete conceptual representation of elementary vehicle actions and reports results obtained by an implementation of this approach from real-world traffic videos.     

Filtering effects of queueing systems on end-to-end network performance

A network is a mesh connection of network elements (NEs), each of which can be characterized as a single server queueing system. Upon arrival to the single server queueing system, a customer may see another customer already in service, and yet others may be in the queue. The new arrival either joins the queue, or is lost if there is no room available. By studying the traffic parameters at the ingress and egress of a generic NE, it is possible to deduce the filtering effect of a single server queueing system. On the assumption of exponential on–off traffic flows, we have derived equations to recursively compute the output traffic parameters as a function of the input traffic parameters. Since the output from the ith NE is also the input to the (i+1)st NE, the recursive equations allow us to assess the end-to-end network performance. Simulation results are used to gauge the accuracy of the analytical approach.     

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。     

车路协同路侧感知融合方法的研究

随着智能网联汽车技术和产业的不断发展,智能网联汽车逐渐成为人们交通出行的选择之一。但受智能网联汽车自身环境感知系统对特定道路交通场景信息处理的局限,无法实现在所有行驶工况下安全高效的运行,其需车路协同路侧感知技术的辅助方能更安全高效的运行。海量的车路协同感知数据是城市道路和高速公路车路协同、运行分析和科学管理的宝藏,理解和分析这些数据是车路协同路侧感知融合的关键。面对车路协同路侧多传感器的不同数据,如何高效准确地挖掘和提取雷达、视频在不同时间、不同空间维度的数据,实现对重点交通场景(如视野盲区、急转弯道、隧道、桥梁)和交通事件、环境、设施安全等的雷达、视频数据进行快速融合检测、识别与检索,通过蜂窝车联网C-V2X网络在一定时延范围内有效地将路侧感知融合结果数据发送给智能网联汽车,确保其安全高效的行驶,是面向智能网联汽车辅助驾驶的车路协同路侧感知融合的关键问题。基于智能网联汽车其自身环境感知能力,对道路智能基础设施感知网络中的多传感器融合方法进行研究分析,提出了基于误差方差的多传感器融合算法,与非智能道路相比,其效率更高,更加智能化,可有效解决道路交通运行环境中存在的常见问题,为人们提供更加安全、高效、优质的交通出行服务。