TTCAN系统及其在分布式车辆网络中的应用

在车辆总线技术研究的基础上分析了CAN通信应用,对CAN总线仲裁消息发送延迟、不相容行为消息错误进行了详细研究,并通过试验定量分析事件触发CAN在实时系统应用中的不足。为克服传统CAN通信不足引入时间触发通信协议,阐述了时间触发CAN（TTCAN）系统结构、定时机制、容错等关键技术,并对车辆其它类似总线进行了分析,最后对TTCAN在分布式车辆网络中的应用前景进行了展望。     

Web Services中的隐私保护

Web Services技术将成为动态电子商务的主流技术,但在提供具有很大吸引力的好处的同时,也对安全性提出了严峻的挑战。只有全面、灵活、方便地解决的安全问题才能使其技术更好更广泛地参与到商业应用中。基于这种情况,本文提出一种以数字隐私证书、数据过虑器和移动隐私保护代理为基础,在政府Web Services中新的保护隐私的技术方法。     

基于ECDIS的分布式AIS数据接收平台的研究

融合了分布式AIS数据的ECDIS是航运安全管理的新型技术平台。论文论述了基于ECDIS的分布式AIS数据接收平台的原理架构与网络环境的选择,设计了系统网络环境下的通信模式,实现了分布式AIS数据的接收。     

线控转向系统故障检测及容错控制协同设计

为解决线控转向系统故障可能导致车辆失控的问题,提出一种故障检测及容错控制协同设计方法。首先,建立了包含线控转向系统加性故障的车辆动力学模型;其次,联合车辆动力学模型及故障检测/容错控制器,建立跟踪误差闭环控制系统;然后,求解满足闭环系统H∞性能的线性矩阵不等式,得到故障检测/容错控制器参数;最后,基于dSPACE Full-Size HIL进行硬件在环仿真测试。结果表明,该方法可快速检测出转向系统故障,同时实现了车辆的容错控制。     

A control theoretic formulation of green driving strategies based on inter-vehicle communications

Various green driving strategies have been proposed to smooth traffic flow and lower pollutant emissions and fuel consumption in stop-and-go traffic. In this paper, we present a control theoretic formulation of distributed, cooperative green driving strategies based on inter-vehicle communications (IVCs). The control variable is the advisory speed limit, which is designed to smooth a following vehicle’s speed profile without changing its average speed. We theoretically analyze the performance of a constant independent and three simple cooperative green driving strategies and present three rules for effective and robust strategies. We then develop a distributed cooperative green driving strategy, in which the advisory speed limit is first independently calculated by each individual vehicle and then averaged among green driving vehicles through IVC. By simulations with Newell’s car-following model and the Comprehensive Modal Emissions Model (CMEM), we demonstrate that such a strategy is effective and robust independently as well as cooperatively for different market penetration rates of IVC-equipped vehicles and communication delays. In particular, even when 5% of the vehicles implement the green driving strategy and the IVC communication delay is 60 s, the fuel consumption can be reduced by up to 15%. Finally we discuss some future extensions.     

在辽宁省高速公路预防性养护微表处填补车辙的施工

稀浆封层技术是一种性能优良的路面养护措施,随着聚合物改性乳化沥青的迅猛发展,以及施工技术的改进,又出现了聚合物改性乳化沥青稀浆封层,即微表处。本文通过对沈海高速公路预防性微表处的工程实践的探讨,对以后辽宁省等高纬度地区的高速公路预防性微表处项目的研究与施工具有一定的指导意义。     

高回弹聚氨酯泡沫的研究和应用

概述了聚氨酯高回弹泡沫优越性,重点阐述了高回弹泡沫的配方设计,根据多年的实际经验总结了冷模塑高回弹泡沫生产过程中容易出现的问题及其解决办法。     

An extended coordinate descent method for distributed anticipatory network traffic control

Anticipatory optimal network control can be defined as the practice of determining the set of control actions that minimizes a network-wide objective function, so that the consequences of this action are taken in consideration not only locally, on the propagation of flows, but globally, taking into account the user’s routing behavior. Such an objective function is, in general, defined and optimized in a centralized setting, as knowledge regarding the whole network is needed in order to correctly compute it. This is a strong theoretical framework but, in practice, reaching a level of centralization sufficient to achieve said optimality is very challenging. Furthermore, even if centralization was possible, it would exhibit several shortcomings, with concerns such as computational speed (centralized optimization of a huge control set with a highly nonlinear objective function), reliability and communication overhead arising.The main aim of this work is to develop a decomposed heuristic descent algorithm that, demanding the different control entities to share the same information set, attains network-wide optimality through separate control actions.     

Distributed model predictive control for railway traffic management

Every day small delays occur in almost all railway networks. Such small delays are often called “disturbances” in literature. In order to deal with disturbances dispatchers reschedule and reroute trains, or break connections. We call this the railway management problem. In this paper we describe how the railway management problem can be solved using centralized model predictive control (MPC) and we propose several distributed model predictive control (DMPC) methods to solve the railway management problem for entire (national) railway networks. Furthermore, we propose an optimization method to determine a good partitioning of the network in an arbitrary number of sub-networks that is used for the DMPC methods. The DMPC methods are extensively tested in a case study using a model of the Dutch railway network and the trains of the Nederlandse Spoorwegen. From the case study it is clear that the DMPC methods can solve the railway traffic management problem, with the same reduction in delays, much faster than the centralized MPC method.     

Using BDI agents to improve driver modelling in a commuter scenario

The use of multi-agent systems to model and to simulate real systems consisting of intelligent entities capable of autonomously co-operating with each other has emerged as an important field of research. This has been applied to a variety of areas, such as social sciences, engineering, and mathematical and physical theories. In this work, we address the complex task of modelling drivers’ behaviour through the use of agent-based techniques. Contemporary traffic systems have experienced considerable changes in the last few years, and the rapid growth of urban areas has challenged scientific and technical communities. Influencing drivers’ behaviour appears as an alternative to traditional approaches to cope with the potential problem of traffic congestion, such as the physical modification of road infrastructures and the improvement of control systems. It arises as one of the underlying ideas of intelligent transportation systems. In order to offer a good means to evaluate the impact that exogenous information may exert on drivers’ decision making, we propose an extension to an existing microscopic simulation model called Dynamic Route Assignment Combining User Learning and microsimulAtion (DRACULA). In this extension, the traffic domain is viewed as a multi-agent world and drivers are endowed with mental attitudes, which allow rational decisions about route choice and departure time. This work is divided into two main parts. The first part describes the original DRACULA framework and the extension proposed to support our agent-based traffic model. The second part is concerned with the reasoning mechanism of drivers modelled by means of a Beliefs, Desires, and Intentions (BDI) architecture. In this part, we use AgentSpeak(L) to specify commuter scenarios and special emphasis is given to departure time and route choices. This paper contributes in that respect by showing a practical way of representing and assessing drivers’ behaviour and the adequacy of using AgentSpeak(L) as a modelling language, as it provides clear and elegant specifications of BDI agents.