山区高速公路事故多发点的组合鉴别方法

基于国内外交通事故鉴别方法,结合山区高速公路特点及其事故特点,提出改进的鉴别指数与非定长分段法相结合的适合山区高速公路的混合鉴别方法,最后在包茂高速(西安—榆林)铜川段进行实例验证,证明了该方法的有效性。     

基于扩展卡尔曼滤波的船舶电力推进系统仿真

针对船舶电力推进系统中采用传统直接转矩控制存在磁链和转矩脉动较大,以及速度传感器的使用降低了系统可靠性,增加了系统成本等问题.提出采用扩展卡尔曼滤波器精确估计电机的定子磁链和电机转速,间接估计转矩,从而实现永磁同步电机的无速度传感器直接转矩控制.保持了直接转矩控制的转矩快速响应和系统鲁棒性强的优点,降低磁链和转矩脉动,减小了系统因电机参数的变化和负载扰动带来的影响.仿真结果表明,基于扩展卡尔曼滤波的船舶电力推进系统具有良好的转速和转矩控制性能.     

钢筋混凝土框架结构震后快速鉴定方法

提出一种震后框架结构安全性快速评估的方法.该方法在分析框架结构常见震害的基础上,对地震中构件的破坏程度提出了定量和定性的判断标准;利用模糊数学方法对各类构件进行安全等级评价,形成隶属度判断矩阵;采用层次分析法对各类构件、结构层进行权重评价;依据等级隶属度判断矩阵和各类构件、结构层的权重进行综合分析,得出整体结构的安全性评价.该方法简单易行,层次清晰,鉴定结果较为真实准确.依据该方法,可编制计算软件,使鉴定工作程序化,有效消减鉴定工作量,为震后紧急情况下应急救援政策的制定、救灾措施的实施和开展提供依据.     

基于优化SVM的城市快速路网交通流状态判别

以交通流率、速度和占有率为输入参数,采用交叉验证法优化模型惩罚参数C和核函数参数γ,建立以径向基为核函数的支持向量机模型,判断道路断面交通流状态;结合设计的道路网综合状态指数,依据自由流、拥挤流和阻塞流状态下占有率划分区间,构建城市快速路网交通流状态判别方法;最后以某一区域路网为例,进行了实证性研究．结果表明：该方法对道路断面交通流状态判别精度可达92．22％;同时能够实现道路网范围内对自由流、拥挤流和阻塞流状态的判别,判别精度可达86．67％．     

交通运输事故隐患跟踪管理系统开发研究

事故隐患跟踪管理系统主要针对交通运输部门对隐患识别、隐患上报、隐患控制、隐患消除进行全过程的隐患跟踪管理,并以此为依据开发基于B/S实现的交通运输部门隐患跟踪管理系统,以实现交通运输部门不同层级的企事业单位对隐患管理的网络化办公,提高交通行业的安全隐患管理效率,做到防范于未然,把安全隐患消灭在萌芽状态。     

基于800/900MHz频段的组合式电子收费车道设计

根据目前国内高速公路联网收费实际和技术条件,结合重庆市电子车牌项目作为省级区域国家级信息化试点的要求,提出了将基于800/900 MHz频段的组合式电子收费车道作为ETC(Electronic Toll Collection,电子不停车收费)快速通行手段的设计方案。该方案与目前MTC(Manual Toll Collection,人工半自动收费)的技术条件和实际环境兼容,并且投资成本低,具有较高的实用性和可操作性。     

Estimation of road profile variability from measured vehicle responses

When assessing the statistical variability of fatigue loads acting throughout the life of a vehicle, the question of the variability of road roughness naturally arises, as both quantities are strongly related. For car manufacturers, gathering information on the environment in which vehicles evolve is a long and costly but necessary process to adapt their products to durability requirements. In the present paper, a data processing algorithm is proposed in order to estimate the road profiles covered by a given vehicle, from the dynamic responses measured on this vehicle. The algorithm based on Kalman filtering theory aims at solving a so-called inverse problem, in a stochastic framework. It is validated using experimental data obtained from simulations and real measurements. The proposed method is subsequently applied to extract valuable statistical information on road roughness from an existing load characterisation campaign carried out by Renault within one of its markets.     

数字化汽车标准信源技术及其在公路交通管理中的应用研究

数字化汽车标准信源体系的建立是解决我国公路交通管理难题的有效方式。以800/900MHz无源RFID技术可建立起数字化汽车标准信源体系平台,通过建立该平台,可形成不停车收费及路径识别的应用系统,该系统不但可以实现不停车收费及路径识别功能,还可以进行一系列的拓展应用,提高我国公路交通的管理水平。     

Adhesion estimation at the wheel–rail interface using advanced model-based filtering

The railway industry in the UK is currently expanding the use of condition monitoring of railway vehicles. These systems can be used to improve maintenance procedures or could potentially be used to monitor current vehicle running conditions without the use of cost prohibitive sensors. This paper looks at a novel method for the online detection of areas of low adhesion in the wheel/rail contact that cause significant disruption to the running of a network, particularly in the autumn season. The proposed method uses a Kalman–Bucy filter to estimate the creep forces in the wheel–rail contact area; post-processing is then applied to provide information indicative of the actual adhesion level. The algorithm uses data that, in practice, would be available from a set of modest cost inertial sensors mounted on the vehicle bogie and wheel-sets. The efficacy of the approach is demonstrated using simulation data from a nonlinear dynamic model of the vehicle and its track interface.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.