Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model

Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.     

A yaw-moment control method based on a vehicle's lateral jerk information

Previously, a new control concept called ‘G-vectoring control (GVC)’ to improve vehicle agility and stability was developed. GVC is an automatic longitudinal acceleration control method that responds to vehicle lateral jerk caused by the driver's steering manoeuvres. In this paper, a new yaw-moment control method, which generates a stabilising moment during the GVC command and has positive acceleration value and the driver's accelerator pedal input is zero, was proposed. A new hybrid control, which comprises GVC, electric stability control and this new control, was constructed, and it was installed in a test vehicle and tested on a snowy surface. The very high potential for improvement in both agility and stability was confirmed.     

Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses

Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.     

Influence of some vehicle and track parameters on the environmental vibrations induced by railway traffic

A study is performed on the influence of some typical railway vehicle and track parameters on the level of ground vibrations induced in the neighbourhood. The results are obtained from a previously validated simulation framework considering in a first step the vehicle/track subsystem and, in a second step, the response of the soil to the forces resulting from the first analysis. The vehicle is reduced to a simple vertical 3-dof model, corresponding to the superposition of the wheelset, the bogie and the car body. The rail is modelled as a succession of beam elements elastically supported by the sleepers, lying themselves on a flexible foundation representing the ballast and the subgrade. The connection between the wheels and the rails is realised through a non-linear Hertzian contact. The soil motion is obtained from a finite/infinite element model. The investigated vehicle parameters are its type (urban, high speed, freight, etc.) and its speed. For the track, the rail flexural stiffness, the railpad stiffness, the spacing between sleepers and the rail and sleeper masses are considered. In all cases, the parameter value range is defined from a bibliographic browsing. At the end, the paper proposes a table summarising the influence of each studied parameter on three indicators: the vehicle acceleration, the rail velocity and the soil velocity. It namely turns out that the vehicle has a serious influence on the vibration level and should be considered in prediction models.     

An agent-based approach to modelling driver route choice behaviour under the influence of real-time information

This paper presents an agent-based approach to modelling individual driver behaviour under the influence of real-time traffic information. The driver behaviour models developed in this study are based on a behavioural survey of drivers which was conducted on a congested commuting corridor in Brisbane, Australia. Commuters’ responses to travel information were analysed and a number of discrete choice models were developed to determine the factors influencing drivers’ behaviour and their propensity to change route and adjust travel patterns. Based on the results obtained from the behavioural survey, the agent behaviour parameters which define driver characteristics, knowledge and preferences were identified and their values determined. A case study implementing a simple agent-based route choice decision model within a microscopic traffic simulation tool is also presented. Driver-vehicle units (DVUs) were modelled as autonomous software components that can each be assigned a set of goals to achieve and a database of knowledge comprising certain beliefs, intentions and preferences concerning the driving task. Each DVU provided route choice decision-making capabilities, based on perception of its environment, that were similar to the described intentions of the driver it represented. The case study clearly demonstrated the feasibility of the approach and the potential to develop more complex driver behavioural dynamics based on the belief–desire–intention agent architecture.     

新型船舶运动控制平台及海上试验

研制出一艘以水动力学相似准则缩尺的无人驾驶遥控模型船，模型船装有PC／104为主控计算机的船舶控制器，模型船由岸上主PC机遥控，用Honeywell公司的HMR3000碰罗经测量航导，用差分GPS系统定位，自主控制和推进，该遥控篡自治船舶运动控制已经进行了两次海上试验并取得成功，本文介绍该平台的系统设计，控制系统硬件和软件和海上试验经。     

AutoLISP程序设计的若干问题

本文剖析了 AutoLISP 程序的结构特点,运行长大程序的措施和数据文件的组织等三个方面的问题.     

系统保证管理在成都地铁机电系统建设中的应用

系统保证管理在国外多个轨道交通项目中得到了成功应用。成都地铁立足于国内建设城市轨道交通项目的现状,结合自身实际,为更好地实现安全和效益的目标,在机电系统的建设过程中全面开展了系统保证管理。提出了系统保证管理的基本概念和主要工作内容;介绍了成都地铁1号线机电系统实施系统保证管理的工作进程。成都地铁1号线系统保证工作开展近2年来,取得了显著成果,为国内城市轨道交通行业引入系统保证管理、促进国内城市轨道交通行业发展起到了借鉴和推动作用。     

Reliability analysis of repairable systems using stochastic point processes

In order to analyze the failure data from repairable systems, the homogeneous Poisson process(HPP) is usually used. In general, HPP cannot be applied to analyze the entire life cycle of a complex, re-pairable system because the rate of occurrence of failures (ROCOF) of the system changes over time rather thanremains stable. However, from a practical point of view, it is always preferred to apply the simplest methodto address problems and to obtain useful practical results. Therefore, we attempted to use the HPP model toanalyze the failure data from real repairable systems. A graphic method and the Laplace test were also usedin the analysis. Results of numerical applications show that the HPP model may be a useful tool for the entirelife cycle of repairable systems.     

路段平均行程时间估计方法

为了有效利用线圈检测数据,精确估计路段平均行程时间,提出了一种路段平均行程时间估计方法。将路段平均行程时间分为平均行驶时间、平均排队时间和平均通过路口时间三部分。考虑线圈埋设的特点,通过估计平均行驶速度得到平均行驶时间。用分段时齐Poisson过程描述车辆驶入路段过程和驶离过程,用Markov排队模型描述车辆排队过程,用生灭过程描述排队车辆数,得到车辆排队模型,计算了路段有、无初始排队的平均排队时间。基于选取与路口相关的饱和流率和平均车长,计算了平均通过路口时间。计算结果表明:平均行程时间估计值与实测值的误差小于12%,说明路段平均行程时间估计方法可行。