基于多媒体时钟的定时控制

由于Windows平台图形界面的易操作，使得在各行各业得到迅速的普及。这在工业控制领域也不例外，但是由于Windows操作系统在大部分情况下是以队列形式工作的，使得在Windows下实现实时控制成为了难题。利用多媒体时钟可以很好地实现Windows实时控制。     

船用防避台风决策系统基本技术

运用相对运动原理求出船舶避离台风的基础可航扇区；根据预报的风浪场，计算出产生船舶“谐摇”的扇区；根据船舶性能和波浪预报，用等航时线法求出最少航时扇区；采用最优控制理论，在基础可航扇区内剔除“谐摇”扇区，并与最少航时扇区进行优化，最终得到既安全又省时的船舶避离台风的最佳可航扇区。把防避台风的整个决策过程开发成业务化的软件包，从技术上帮助船长加快避台决策速度，提高避台实施精度，达到安全经济的防避台风的目的。     

高速铁路电务基本维修组织的探索与实践

工区是电务系统最基本的维修组织。提速、普速铁路点多线长的特点以及设备用修矛盾突出、结合部问题多、设备老化磨损严重、跨站跨区间作业存在交通安全问题等原因,决定了传统的小型化、分散化的信号工区设置方式更能适应提速普速铁路设备维修需要。本文在分析提速普速铁路电务维修管理模式存在问题、高速铁路电务维修工作特点的基础上,提出按照集中、融合的管理思路推进高速铁路电务维修管理模式的探索,在综合维修管理方面迈出电务维修管理改革新的步伐。     

专用铁路综合数据通信网建设方案探讨

铁路综合数据通信网,是铁路信息化建设的基础平台,建设时首先应完善铁路基础通信网,然后从业务需求、网络结构、技术方案、网络管理、安全策略等几个方面综合考虑,合理规划,有效实现资源最大化的目标,使专用铁路信息化建设更科学、更合理。     

CTC车站基层网的调试与故障处理

CTC（调度集中）系统,可以使远在千里之外的调度员对信号设备进行控制,对列车运行进行指挥、管理。网络是CTC系统最关键的平台,为此就太中银铁路的CTC车站基层网的安装调试及故障处理进行总结,以兹参考。     

Delivering improved alerts,warnings, and control assistance using basic safety messages transmitted between connected vehicles

When vehicles share their status information with other vehicles or the infrastructure, driving actions can be planned better, hazards can be identified sooner, and safer responses to hazards are possible. The Safety Pilot Model Deployment (SPMD) is underway in Ann Arbor, Michigan; the purpose is to demonstrate connected technologies in a real-world environment. The core data transmitted through Vehicle-to-Vehicle and Vehicle-to-Infrastructure (or V2V and V2I) applications are called Basic Safety Messages (BSMs), which are transmitted typically at a frequency of 10 Hz. BSMs describe a vehicle’s position (latitude, longitude, and elevation) and motion (heading, speed, and acceleration). This study proposes a data analytic methodology to extract critical information from raw BSM data available from SPMD. A total of 968,522 records of basic safety messages, gathered from 155 trips made by 49 vehicles, was analyzed. The information extracted from BSM data captured extreme driving events such as hard accelerations and braking. This information can be provided to drivers, giving them instantaneous feedback about dangers in surrounding roadway environments; it can also provide control assistance. While extracting critical information from BSMs, this study offers a fundamental understanding of instantaneous driving decisions. Longitudinal and lateral accelerations included in BSMs were specifically investigated. Varying distributions of instantaneous longitudinal and lateral accelerations are quantified. Based on the distributions, the study created a framework for generating alerts/warnings, and control assistance from extreme events, transmittable through V2V and V2I applications. Models were estimated to untangle the correlates of extreme events. The implications of the findings and applications to connected vehicles are discussed in this paper.     

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle.     

How to Use Driving Simulators Properly: Impacts of Human Sensory and Perceptual Capabilities on Visual Fidelity

Fidelity has been a critical concern of researchers throughout the history of driving simulation. Understanding the limits of a driving simulation system is a prerequisite for conducting valid driving simulator studies. This paper proposes a novel and interdisciplinary methodology to ensure validity of studies using driving simulators (primarily for traffic control devices and other object detection tasks) based on the visual limits of human sensory and perceptual capabilities, and the characteristics of raster graphics. This methodology decomposes the perceptual issues of a stimulus into perceptual issues of different visual properties like luminance, hue, or text of the stimulus. By systematically analyzing the mechanism of human vision in driving simulators, the perceptual principle is proposed to ensure perceivable visual details in human-in-the-loop driving simulation systems. Additionally, the graphic principle is proposed to ensure perceivable features of a target object in the virtual driving environment. Both principles quantify the minimum requirements of visual fidelity with two measurements: angular resolution and matrix dimensions. The enriched results from existing pertinent studies are analyzed and organized to yield support of both principles. This research focuses on the minimum requirements for four factors; namely the visual acuity of drivers, the specifications of display systems, the configurations of graphics systems, and the design of virtual scenarios, as well as the relationship among all these factors to assess the visual fidelity in driving simulation systems. Within the realm of human perception, this work can provide criteria for proper design, calibration, and usage of driving simulators.     

汽车ACC毫米波雷达和视觉传感器目标级融合方法研究

自适应巡航控制系统是实现未来智能化汽车辅助驾驶的重要功能之一,以往该系统主要采用毫米波雷达感知周围环境,但是容易出现较多的误识别和漏识别情况。针对现存的问题,文章研究了自适应巡航感知系统,不同于以往单一雷达的方案,本设计采用毫米波雷达和视觉传感器融合的办法改善感知系统的性能。通过搭建电动车传感器数据采集系统,编写CAN通信报文解析程序,分析毫米波雷达和视觉传感器特性等,完成了对雷达和视觉信号的采集及处理,实现了感知系统目标级融合。并在巡航和跟车工况下进行离线仿真,验证了目标级融合方案能够有效地提高感知系统的准确性和合理性。     

基于深度学习的智能车辆视觉里程计技术发展综述

针对基于模型的视觉里程计在光照条件恶劣的情况下存在鲁棒性差、回环检测准确率低、动态场景中精度不够、无法对场景进行语义理解等问题,利用深度学习可以弥补其不足。首先,简略介绍了基于模型的里程计的研究现状,然后对比了常用的智能车数据集,将基于深度学习的视觉里程计分为有监督学习、无监督学习和模型法与深度学习结合3种,从网络结构、输入和输出特征、鲁棒性等方面进行分析,最后,讨论了基于深度学习的智能车辆视觉里程计研究热点,从视觉里程计在动态场景的鲁棒性优化、多传感器融合、场景语义分割3个方面对智能车辆视觉里程计技术的发展趋势进行了展望。