铝合金耐压结构设计分析与试验验证

根据水下潜器总体性能要求,按照最小重量原则,结合规范和有限元方法设计工作潜深为500m的铝合金带加强筋形式的圆柱耐压舱,并通过压力筒加压试验,对Ansys有限元静力分析的精确度进行了验证,为耐压结构设计与校核提供重要参考.     

信号控制交叉口左转非机动车过街模式适用性

为分析信号控制交叉口两种左转非机动车过街模式（机动车和非机动车一体化模式以及行人和非机动车一体化模式）的适用条件,在动态交通条件下,定量分析两种模式对典型两相位和四相位信号控制交叉口通行效率和安全性的单独与综合影响。在通行效率方面,选取机动车通行能力作为评价指标;在安全性方面,引入一个可比选交通设计方案的指标——交通当量冲突。通过理论与实例分析,从通行效率和安全性两方面给出了两种模式的适用性。结果表明：两种模式在上述方面均有单独优势;两相位信号控制交叉口,在相应的左转非机动车和机动车交通量条件下两种模式均能同时提高交叉口通行效率和安全性;四相位信号控制交叉口,两种模式均不能同时起到积极作用。     

基于线网全寿命成本控制目标的车辆基地资源共享研究

目前国内各城市都在开展或已经开展资源共享的研究,其中车辆基地资源共享是线网资源共享的重要组成部分。根据《地铁设计规范》规定,车辆基地是保证地铁正常运营的后勤基地。车辆基地的设计范围包括车辆段、综合维修中心、物资总库和培训中心以及必要的办公、生活设施等,是地铁正常运营所必需的设备和设施。通过广泛调研,目前国内大多数城市车辆基地资源共享研究还存在一定的不足,仅集中在车辆大、架修的资源共享,缺乏对车辆基地综合维修中心、物资库等全系统的资源共享研究。创新性地提出基于线网全寿命成本控制目标的车辆基地资源共享研究思路,以沈阳地铁线网车辆基地为例,提出资源共享方案。除大、架修资源共享外,进一步研究物资存储、综合维修、特种车辆等多方面的资源共享,此外,对车辆基地各个单体的配置提出一定的标准化指标。在充分调研国内其他城市地铁建设经验、运营经验以及不同城市车辆基地建设规模的基础上,最终得到沈阳地铁车辆大架修资源共享、物资共享、综合维修共享、特种车辆共享以及场段配置标准化5个方面的研究结论。便于指导沈阳及类似城市地铁在新一轮的建设规划中以及未来线网规划调整中更加合理地分配车辆基地的物资、人力、设备、用地。     

基于行为识别的智能车纵向决策研究

针对智能车纵向决策问题,提出基于环境车辆偏离车道程度识别运动模式的方法;构建动态环境车辆横纵向轨迹预测模型,并求解;构建保持、先行、避让在内的决策集,提出基于预测轨迹的单个车辆决策方法,并基于所有动态环境车辆的决策结果在加速、减速和匀速3 种结果中做出综合决策. 实车实验表明：在直行、换道和转弯运动模式下轨迹预测平均误差分别为0.11,0.29,0.80 m,预测精度较高;复杂动态环境下,本文提供的纵向决策信息提升了智能车行驶的安全性和舒适性.     

道路交通安全水平预测的统计模型

为了评价和预测不同国家的道路交通安全水平,建立通用的统计模型,作者首先分析道路交通事故死亡率与人口规模、机动车保有量、道路里程以及人均GNP之间的相关关系,根据相关性选择影响道路交通事故的因素,并建立万车死亡率与人均GNP之间的函数关系.模型计算结果与校核数据对比分析表明,所提出的模型能够较好地评价和预测一个国家或地区的道路交通安全水平.模型对交通管理者提高交通安全性的科学决策具有重要指导意义.     

基于键图理论对安装EPS系统的车辆操纵稳定性的仿真分析

应用键图理论建立电动助力转向系统(EPS)的模型,将电动助力转向系统的键图模型和matlab的simulink仿真方框图相连接,并利用simulink的可视化功能实现汽车操纵稳定性的可视化仿真,仿真结果表明,将simulink仿真工具箱与键图模型相结合,可使汽车性能分析研究更为直观和简洁.     

Autonomous vehicle perception: The technology of today and tomorrow

Perception system design is a vital step in the development of an autonomous vehicle (AV). With the vast selection of available off-the-shelf schemes and seemingly endless options of sensor systems implemented in research and commercial vehicles, it can be difficult to identify the optimal system for one’s AV application. This article presents a comprehensive review of the state-of-the-art AV perception technology available today. It provides up-to-date information about the advantages, disadvantages, limits, and ideal applications of specific AV sensors; the most prevalent sensors in current research and commercial AVs; autonomous features currently on the market; and localization and mapping methods currently implemented in AV research. This information is useful for newcomers to the AV field to gain a greater understanding of the current AV solution landscape and to guide experienced researchers towards research areas requiring further development. Furthermore, this paper highlights future research areas and draws conclusions about the most effective methods for AV perception and its effect on localization and mapping. Topics discussed in the Perception and Automotive Sensors section focus on the sensors themselves, whereas topics discussed in the Localization and Mapping section focus on how the vehicle perceives where it is on the road, providing context for the use of the automotive sensors. By improving on current state-of-the-art perception systems, AVs will become more robust, reliable, safe, and accessible, ultimately providing greater efficiency, mobility, and safety benefits to the public.     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.     

不同混合动力电动轿车方案的比较与分析

设计了同在离合器前与发动机的动力耦合的ISA并联、电机在离合器后变速器前与发动机的动力耦合的一般并联、类似于Prius的混联三种HEV方案。对这三种方案和原轿车的最高车速、原地起步加速特性等动力性指标和基于ECE＋EUDC的100km油耗和续驶里程等能量经济性指标及排施行性进行了仿真和分析。得到相应结论。