基于D-S证据理论的海战场目标战术意图识别技术研究

目标战术意图识别技术是海战场态势评估领域的一个核心内容,也是威胁估计的基础。根据舰艇指挥决策中的海战场态势评估的需要,提取目标战术意图识别所需要素,然后运用高维空间相似度计算模型,度量目标状态对其意图的支撑程度,最后结合D-C证据理论对目标战术意图进行序贯识别,通过实例验证,取得了较好的效果。该方法能够为解决海战场目标战术意图识别提供新的途径。     

智能网联车辆混行交通流中灯语意图识别模型研究

为使混行交通流下智能网联车辆(Connected and Automated Vehicles, CAV)实现对人工驾驶车辆(Human-driven Vehicle, HV)前照灯灯语意图(Vehicle Headlights Intention, VHI) 的识别，弥补车对车(Vehicle to Vehicle, V2V)和鸣笛意图识别技术的不足，更好地与HV交互沟通，提出CAV对HV的VHI识别模型.模型包括：灯光感知、光数据处理、VHI识别3个模块，灯光感知模块通过RGB(Red-Green-Blue, RGB)和HSV(Hue-Saturation-Value, HSV)颜色空间感知前照灯(Vehicle Headlights, VH)，采用KLT(Kanade-Lucas-Tomasi Tracking，KLT)和车辆匹配算法定位跟踪发出灯语的HV；光数据处理模块采用光通道增益算法计算光辐射通量变化； VHI识别模块基于双层隐马尔可夫模型(Double-layer Hidden Markov Model，DHMM)辨识VH 闪烁次数和HV行驶状态，实现VHI识别.在3种灯语示意典型场景下的实验结果表明：1 s内 VH感知准确率为96.8%，定位跟踪精度小于1°，VHI识别率为96.6%，满足混行交通环境下 CAV对HV驾驶意图的识别要求，基本保证实时性，为混行交通流中CAV自动驾驶决策提供理论依据.     

浅谈农村公路专项调查

根据交通部对农村公路建设新要求,为了合理分配农村公路建设资金,全面准确监测农村公路建设情况,交通部决定开展农村公路专项调查。详细阐述了丹东市农村公路专项调查对象和内容、工作开展的全过程及结果。     

HEV制动意图识别的研究

基于大量的工况数据建立辨识模型,实现了对制动意图的准确识别.在此基础上优化了再生制动的控制策略.仿真结果表明,通过制动意图识别可有效地优化混合动力汽车再生制动的控制策略,从而进一步改善混合动力汽车制动时的驾驶感觉和燃油经济性.     

基于制动意图的电动汽车复合制动系统制动力分配策略研究

文中对制动意图的识别和不同制动意图所要求的不同的制动力分配策略进行研究.通过试验,验证了制动意图识别算法和制动力分配策略的可行性和有效性,为复合制动系统的应用奠定了基础.     

识别驾驶人意图的车道偏离防避有界控制

为了防止车辆偏离车道导致交通事故的发生和避免车道偏离防避系统（Lane Departure Avoidance Systems,LDAS）对驾驶人行为不必要的干预,提出基于中心区操纵特性阈值法和基于D-S（Dempster-Shafer）证据理论的车辆偏离车道驾驶人意图识别准则,并运用CarSim/Simulink联合仿真对比2种识别准则的有效性。建立转向盘角速度为输入的车路模型,设计LDAS滑模转向控制器,基于预瞄点的侧向偏移量和横摆角速度设计LDAS的期望横摆角速度观测器,并与基于道路曲率和预瞄点侧向偏移量的期望横摆角速度的LDAS进行性能对比。运用相平面法确定保证LDAS车辆稳定性的前轮转向角最大值,并基于CarSim/LabVIEW RT硬件在环试验平台验证基于BP神经网络训练获得D-S证据理论的初始概率赋值的驾驶人意图决策算法的有效性。结果表明：所提出的识别准则能够及时识别车辆偏离车道时的驾驶人意图,为LDAS控制器介入赢得了宝贵的时间,所设计的期望横摆角速度观测器具有很好的稳定性,所提出的方法能够有效避免车辆偏离车道。     

石灰粉煤灰混合料强度形成机理及提高早期强度的措施

分析石灰粉煤灰混合料强度的形成机理,并通过理论分析说明提高石灰粉煤灰混合料早期强度的措施,有助于归纳提高石灰粉煤灰混合料早期强度机理的方法。     

Learning User Intention in Networked Mobile Robot Control

Teleoperated networked robot often has unpredictable behaviors due to uncertain time delay from data transmission over Internet. The robot cannot accomplish the desired actions of the remote operator in time, which severely impairs reliability and efficiency of the robot system. This paper investigated a novel approach, learning user intention, to compensate the uncertain time delay with the autonomy of a mobile robot. The user intention to control and operate the robot was modeled and incrementally inferred based on Bayesian techniques so that the desired actions could be recognized and completed by the robot autonomously. Thus the networked robot is able to fulfill the task assigned without frequent interaction with the user, which decreases data transmission and improves the efficiency of the whole system. Experimental results show the validity and feasibility of the proposed method.     

铁路旅客感知的列车服务质量实证研究

探讨铁路客运列车旅客感知服务质量的构成与维度,及其与旅客感知消费价值、旅客满意度、旅客再乘意向以及正面口头传播意向之间的关系。本文以乘坐广深铁路"和谐号"列车的旅客为对象,共收集149份有效问卷,采用结构方程模型分析技术对数据进行分析。研究结果表明:旅客感知的列车服务质量包含3个维度,即硬质量、软质量、列车上所售商品质量;旅客感知的列车服务质量、票价合理性、列车旅行速度对旅客感知的消费价值有显著的正向影响;旅客感知的消费价值对旅客满意度有显著的正向影响;旅客满意度正向影响旅客的再乘意向及其正面口头传播意向。     

Assessing the impact of long-term mobility choice motivation and short-term mobility means connotation on the use intention of electric cars in rural areas

In this study several hypotheses comprising a heuristic framework derived from rational-choice (RC) premises and regarding some potentially influencing variables on future use intention of different vehicle types are tested with a rural area sample. Especially the differentiation between long-term vs. short-term as well as functional/rational vs. extra-functional/emotional motivators is assessed. Results suggest a predominance of functional motivators and rational connotations over extra-functional/emotional ones. The models to check whether short-term or long-term effects dominate did not clearly confirm a predominance of long-term factors as hypothesized. In several regression models a moderating effect of rational short-term connotations on different long-term motivators was found, thus contributing notably to the prediction of future vehicle use-intention. The need for further research and theory-driven modeling is briefly discussed.