A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

Performance‐based near‐optimal vibration control for nonlinear offshore platforms with delayed input

This paper investigates the vibration control problem for offshore platform, where the nonlinear characteristics, delayed input and external wave force are considered in time domain. By introducing a delay‐free reconstructional vector and applying the maximum principle, the original vibration problem for offshore platform is formulated as a nonlinear two‐point‐boundary‐value (TPBV) problem with delayed items. The major contribution of this paper is that a performance‐based near‐optimal vibration control strategy is proposed by solving this nonlinear TPBV problem, which includes a feedback item with offshore platform system state, a feedforward item with wave force state, and a compensator for nonlinear and delayed items with infinite supersensitive component. In particular, the designed compensator is calculated from two group series of linear differential equations by introducing a parameter for expending the Maclaurin series of nonlinear and delay items. Meanwhile, an iterative algorithm is designed to make the proposed vibration control scheme computable based on the control performance in each iterative procedure. Finally, experimental results show that the displacement, velocity and performance index of an employed offshore platform achieved small values under the proposed control strategy and designed algorithm.     

基于国产处理器增量式实时交通流预测算法及实现

针对城市交通难以处理大量数据且实时性差等问题,提出了根据增量式城市交通流数据预测拥堵情况的一种基于国产处理器的L-BFGS(limited-memory BFGS)算法。该算法通过存储向量序列计算Hessian矩阵,改进Two-Loop算法求下降方向,在Spark集群中并行处理时收敛速度快,适用于实时性要求强的城市交通场景。实验结果证明,L-BFGS预测算法完全可以在国产平台上对大规模的实时交通数据流进行快速建模、预测,在改善城市交通管理水平提供有效支撑的同时也丰富了国产芯片的应用领域。     

多时空配送任务驱动的无人车队车辆数优化方法

为解决快递终端配送多时空任务驱动下的最小无人车队车辆数配置问题,提出一种随机优化方法。首先,分析服务时长和等待时长对无人车队行驶路线规划的影响,从而构建最短路径模型;然后,基于二维时空网络构造服务序列网络;其次,通过网络转换将最小无人车队车辆数配置问题转化为网络最大流问题,并建立以车队车辆数最小为目标的最小车队模型;最后,针对模型特征设计一种融合Dijkstra算法和Dinic算法的Dijkstra-Dinic算法来对最小无人车队车辆数配置问题进行求解。在四种不同规模的服务网络中进行仿真实验,实验结果表明：在不同成功服务率下,最小无人车队车辆数与服务网络规模呈正相关,但随等待时长的增加而减少并趋向于稳定;所提算法中所引入的One-stop算子大大提高了搜索效率,所提模型和算法适用于大规模服务网络中的最小车队计算。     

面向交通流量预测的时空超关系图卷积网络

交通流量预测是智能交通系统中的重要研究课题,然而,交通对象（如站点、传感器）之间存在的复杂局部时空关系使得这项研究颇具挑战。尽管以往的一些研究将流量预测问题转化为一个时空图预测问题从而取得了较大的进展,但是它们忽略了交通对象们跨时空维度的直接关联性。目前仍缺乏一种全面建模局部时空关系的方法。针对这一问题,首先提出一种新颖的时空超图建模方案,通过构造一种时空超关系来全面地建模复杂的局部时空关系;然后提出一种时空超关系图卷积网络（STHGCN）预测模型来捕获这些关系用于交通流量预测。在四个公开交通数据集上进行了大量对比实验,结果表明,相比ASTGCN、时空同步图卷积网络（STSGCN）等时空预测模型,STHGCN在均方根误差（RMSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）这三个评价指标上均取得了更优的结果,不同模型运行时间的对比结果也表明,STHGCN有着更高的推理速度。     

基于车体横移振动的高速列车虚拟复合阻尼天棚控制算法研究

高速列车车体横向随机振动由车体的横移振动、侧滚振动和摇头振动三自由度合成,是影响车体横向运行平稳性的关键;为了改善列车横向运行平稳性,提高半主动控制性能;通过建立某型高速列车动力学模型,对车体横向振动特性进行分析,得出横移振动的加剧是造成车体合成横向振动和横向平稳性恶化的主要原因;通过分析在传统天棚阻尼控制算法下分别以车体合成横向振动为反馈和以横移振动为反馈对车体横向振动的控制效果,得出采用以车体横移振动为反馈的传统天棚阻尼控制算法对车体横向振动的抑制效果更佳;在此基础上,提出一种以车体横移振动为反馈的虚拟复合阻尼天棚控制算法,并进行联合仿真分析;结果表明:相比于被动控制,采用虚拟复合阻尼天棚控制算法后,车体合成横向振动加速度峰值、均方根值和平稳性改善率分别达到了46%、43%和19.5%,均高于采用传统天棚阻尼控制算法;可见,采用虚拟复合阻尼天棚控制算法在抑制车体合成横向振动,改善车体横向平稳性方面控制性能更佳。     

基于模型的测试用例生成方法

在大量软件出现的今天,除开软件的功能是否完善外,对软件本身提出了更高的安全性和稳定性要求;一款软件在上线前需要进行大量的测试,以便提升软件的质量;由于开发人员参与了软件的研发及上线流程,导致了看待软件问题的局限性,而测试人员在编写测试用例时,往往由于依据文档的不一致性,使得测试用例的价值大打折扣;并且在实际软件的开发流程中,测试环节与开发严重脱节;往往只是为了出相应的测试报告而去测试,偏离了测试的初衷;针对以上问题,提出基于模型的用例生成方法,能够基于工作流程图、判定表、状态转换等多种测试方法,并在该方法中应用边界值与等价类的思想,够贯穿整个软件研发的生命周期,在软件研发初期就能够参与测试,提出设计方案的不足;并且能够自动生成测试用例,提高测试人员的效率.     

一种无线振动传感器频响带宽提高方法

高频响无线振动传感器是机械设备预测性维护领域不可或缺的信号采集设备,目前机械设备监测应用中常用的无线振动传感器频率响应较低,限制了其在机械设备预测性维护领域的应用;为了进一步提高无线振动传感器频率响应带宽,提出一种提高无线振动传感器频响范围的方法,从结构设计、振动探头设计、信号调理链路设计和模数转换器设计4个方面对传统无线振动传感器进行了改进;实验结果表明,无线振动传感器带宽提高方法将无线振动传感器频率响应带宽提高了7.96 dB;在振动监测领域具有较强的实用性,对智能传感器制造领域具有较好的指导意义.     

基于无线传感网络的爆炸场振动监测系统

爆炸场振动测试中节点分布范围广、数量多,对各测试节点的状态监控及全局管理难以有效实现,传统测试方案中节点部署后难以快速更改状态;针对上述问题,依据模块化、标准化和稳定性原则,设计了一种基于无线技术的振动监测系统;给出了爆炸场振动监测系统的基本功能单元,包括数据采集设备、无线组网设备;数据采集设备具有高精度采集、数据存储、高精度同步、定位定姿等功能;利用无线网桥技术构建了覆盖广域范围的无线网络,支持IEEE802.11 n/ac协议,实现了对远程分布式测试节点的集中管理、状态监控、数据传输,也可对指定测试节点的工作模式进行快速设置,以满足测试需求;测试节点的振动测量范围为0～10 g,室外1公里数据传输;通过模拟测试试验验证了系统的有效性,各项功能满足工程需要.