基于双目视觉计算的车辆跟驰状态实时感知系统

双目视觉技术能够实现目标的识别与距离计算，在自动驾驶领域有很大的应用空间。然而，现阶段双目视觉存在光照干扰、遮挡、弱纹理区域歧义匹配等问题，影响其测量的准确性和可靠性。提出基于双目视觉的跟驰状态实时感知系统，该系统采用基于车辆跟驰模型的扩展卡尔曼滤波方法对车辆跟驰状态进行实时估计，包括跟驰距离、前后车速度差等。通过实际道路试验，证明了该系统能够识别并修正测量数据中的异常值，解决弱纹理区域误匹配问题。试验结果表明：25 mm焦距与12 mm焦距的双目系统跟驰间距测量值的平均误差分别为2.66%与9.14%；在相对速度测量方面，2种焦距系统的测量精度基本相同，平均误差均为1 m·s^-1左右。所提出的方法在自动驾驶车辆环境感知领域有较好的应用前景。     

铁路枢纽折角车流数值变化的动态模拟

以解编车流作业规则和系统资源限制为约束,构造不同目标函数下铁路枢纽编组站作业分工数学模型,给出折角车流理论最大值和最小值及相应编组站作业分工方式及车流条件。基于折角车流变化的复杂性和多样性,将外部车流环境视为一个动态的随机输入,以特定铁路枢纽为背景用计算机模拟技术和遗传算法计算不同条件下折角车流数值解。通过对模拟数据趋势变化比较分析,提出铁路枢纽运输组织不应过分追求折角车流最小等结论。     

内置式永磁同步电动机无传感器速度控制和转子初始位置估计方法的应用及实验研究

针对内置式永磁同步电动机传动提出了一种新的无速度传感器控制和转子初始位置估计方法.在内置式永磁同步电动机旋转过程中,采用扩展卡尔曼滤波方法,仅需测量电机定子的电压和电流,便可得到转速和转子位置的估计值.对内置式永磁同步电动机,采用扩展卡尔曼滤波方法的主要困难在于在静止坐标系中其动态模型的复杂性.由于磁路的不对称性,在静止坐标系中建内置式永磁同步电动机的模型比建表面式永磁同步电动机模型更复杂.在无传感器的传动系统中,电机的起动过程是一个问题,因为在起动之前没有任何信息可以获得.转子初始位置估计是在电机静止时,在瞬间对定子绕组施加合适的电压脉冲序列,通过测量峰值电流获得转子位置信息.利用磁饱和效应对凸极性的影响来区分南北磁极.给出基于浮点数字信号处理器TMS320C31平台得到的内置式永磁同步电动机实验结果.     

Performance estimation model of a torque converter part I: Correlation between the internal flow field and energy loss coefficient

The objective of this paper is to improve the performance estimation model of the internal flow field of a torque converter. Compared with performance experiment results, the converter based on the one-dimensional model does not satisfy the performance requirements demanded in practice. Therefore, we need to develop more predictable and reliable performance estimation models. In order to obtain shape information on three-dimensional blade geometry, a process of reverse engineering conducts a torque converter assembly, impeller, turbine and stator. In addition, a CFD simulation including mesh generation and post-processing was carried out to extract equivalent parameters from the internal flow field. The internal flow field can be explained by analyze the correlation between a performance estimation model and CFD analysis. The equivalent performance model adopts the variation of energy loss coefficients for a given operating condition according to the application of a changing energy loss coefficient by the least mean squares method. The estimated equivalent model improves the agreement in performance between experiments and the theoretical model. This model can reduce the error to within about 3 percent. Furthermore, this procedure for predicted performance achieves eminence in the estimation of the capacity factor.     

Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing

This paper reports on real data testing of a real-time freeway traffic state estimator, with a particular focus on its adaptive capabilities. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic macroscopic traffic flow modeling and extended Kalman filtering. One major innovative feature of the traffic state estimator is the online joint estimation of important model parameters (free speed, critical density, and capacity) and traffic flow variables (flows, mean speeds, and densities), which leads to three significant advantages of the estimator: (1) avoidance of prior model calibration; (2) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (3) enabling of incident alarms. These three advantages are demonstrated via suitable real data testing. The achieved testing results are satisfactory and promising for subsequent applications.     

铁道车辆液压减振器卸荷速度选取方法

为了精确选取铁道车辆各类液压减振器的卸荷速度,提出了一种选取卸荷速度的2σ方法。通过试验测试和动力学仿真计算,得到液压减振器两端的相对速度,并进行概率统计分析、参数估计和概率分布假设检验。在相对速度属正态分布的前提下,计算相对速度的2σ值,并作为减振器的卸荷速度。计算结果表明:通过该方法确定卸荷速度的液压减振器可使95%的车辆低频振动得到衰减,5%的高频振动得到过滤。     

基于遗传支持向量机的综合管廊土建工程造价估算方法研究

综合管廊工程造价估算具有影响因素多、小样本及非线性的特点,传统的造价估算方法对于这类问题无法得到准确结果。针对这一问题,提出一种基于遗传支持向量机的综合管廊造价估算模型。分析综合管廊工程的具体特征,运用支持向量机建立工程造价与影响因子之间的非线性映射关系,利用遗传算法对支持向量机进行参数优化并对综合管廊的工程造价进行估算。以16条已建综合管廊工程作为数据库样本,对该模型进行分析验证。样本测试结果显示,利用该模型可将估算误差控制在10%以内,验证了模型估算的可行性。     

Vehicle response-based track geometry assessment using multi-body simulation

The assessment of the geometry of railway tracks is an indispensable requirement for safe rail traffic. Defects which represent a risk for the safety of the train have to be identified and the necessary measures taken. According to current standards, amplitude thresholds are applied to the track geometry parameters measured by recording cars. This geometry-based assessment has proved its value but suffers from the low correlation between the geometry parameters and the vehicle reactions. Experience shows that some defects leading to critical vehicle reactions are underestimated by this approach. The use of vehicle responses in the track geometry assessment process allows identifying critical defects and improving the maintenance operations. This work presents a vehicle response-based assessment method using multi-body simulation. The choice of the relevant operation conditions and the estimation of the simulation uncertainty are outlined. The defects are identified from exceedances of track geometry and vehicle response parameters. They are then classified using clustering methods and the correlation with vehicle response is analysed. The use of vehicle responses allows the detection of critical defects which are not identified from geometry parameters.     

机动目标跟踪典型算法评述

机动目标跟踪算法可以划分为两种：极大似然法和贝叶斯估计法，前者通过对目标量测序列的分析，采用与当前目标运动模式匹配度最高（极大似然性）的模型（包括系统模型和观测模型）对目标状态进行跟踪和估计；后者则首先假定一组模型，再根据量测序列进一步估计出其中每个模型的匹配概率，最后采用贝叶斯公式对全部或部分模型估计进行综合。在此框架下，论文对其中的一些典型算法进行总结和分析。