复合动力超长续航无人艇系统的设计与实现

为实现无人艇在海上追踪、搜救、巡逻、反潜等任务中的超长续航,避免因油料限制而被迫任务中断,设计了一套以风能、太阳能为动力的无人艇系统,利用风帆为无人艇推进,太阳能为船上电控设备提供电能。该系统由无人艇和岸基监控平台组成,无人艇可通过python开发的数据采集模块和算法模块实时采集环境数据、计算、生成并下达控制指令,在风区内可超长续航自主航行,实现全风向利用；岸基监控平台可实时监控无人艇状态,并进行自主/遥控模式切换。对系统进行了测试,通讯连接试验的可靠性高；无人艇航行试验验证了全风向自主航行的可行性,运行轨迹与仿真结果基本重合；太阳能供电分析理论上验证了风区超长续航的可实现性。     

A comparison of modelled and real-life driving profiles for the simulation of railway vehicle operation

A key factor in determining the performance of a railway system is the speed profile of the trains within the network. There can be significant variation in this speed profile for identical trains on identical routes, depending on how the train is driven. A better understanding and control of speed profiles can therefore offer significant potential for improvements in the performance of railway systems. This paper develops a model to allow the variability of real-life driving profiles of railway vehicles to be quantitatively described and predicted, in order to better account for the effects on the speed profile of the train and hence the performance of the railway network as a whole. The model is validated against data from the Tyne and Wear Metro, and replicates the measured data to a good degree of accuracy.     

船舶涂装爬壁机器人系统研究及集成应用

提出一种大型船舶开放性工作面涂装爬壁机器人系统理论设计与应用方法,研究系统本体运行机理并获得满足工作特性的两项必要条件,即轮辐磁吸力及双轮驱动力矩的极小值条件。建立涂装机器人系统的功能组成及控制机理,并结合开放性工作面开展无线定位及自主规划方法研究,形成能够适应喷涂作业的机器人在线路径规划算法。并结合实际开展涂装系统集成应用测试,检测并采集系统运行过程中动力学参数,系统空间坐标轨迹路线等,实现了涂装工作路径的实时在线规划。通过试验验证了文中的系统力学特性分析及定位方法的有效性。     

船舶电缆自动化敷设系统设计与实现

在船舶电缆敷设过程中,很难借助有效的工装提高劳动效率,低空间的高强度作业存在严重的安全风险。以船舶电缆自动化敷设系统为分析对象,介绍系统组成和系统接口,采用比例积分微分(Proportion Integration Differentiation,PID)算法计算系统行程,通过PROFINET总线完成数据传输,以人机界面(Human Machine Interface,HMI)实现系统性能要求。在风电安装平台上的实际运用表明,该系统可解决造船行业劳动力成本高和安全风险大的问题。     

Socially optimal electric driving range of plug-in hybrid electric vehicles

This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.     

面向智能车辆的现役道路设施行驶适应性研究综述

为了总结面向智能车辆的现役道路设施行驶适应性，即现役道路基础设施承载智能车辆行驶的适宜程度，阐述自主智能驾驶定义与驾驶自动化等级分类，在此基础上剖析不同等级间的人机功能差异，并分别从感知层、感知-决策层、决策-控制层探讨与道路设计要素相关联的人机功能差异，通过归纳总结智能车辆与道路几何要素、路面性能及其他道路要素(如道路标线)的相互作用机制研究，从道路工程角度及其他道路要素方面回顾该领域的研究现状，指出存在的问题和未来发展方向。研究结果表明：相比传统车辆，配置高等级自动驾驶系统的智能车辆对现役道路设施行驶适应性最高，主动安全系统次之，而驾驶辅助及有条件自动驾驶系统适应性不足。而目前研究主要问题包括：难以归纳、标定不同驾驶自动化等级间的人机功能差异及其对于道路设计参数的需求设计值；测试道路场景条件过于理想，考虑的驾驶自动化等级单一，试验规模和样本有限；道路几何、路面性能以及道路标志、标线等道路要素与智能车辆间的相互作用机制研究不足，缺乏与不同道路场景相匹配的智能车辆驾驶特征数据的获取手段。因此建议：重视并推动与道路设计要素相关联的关键人机功能差异指标信息共享；联合高保真且可交互的道路场景、高精度感知传感器物理模型、车辆动力学模型及微观交通流模型，利用测试场景自动化生成、极限工况场景搜寻与泛化等技术开展智能驾驶虚拟测试，突破现有研究的深度和广度；探索反映不同等级智能车辆的道路行驶适应性特征指标与评价标准，精准、有效地评估预测复杂道路场景及不利道路条件下的行驶适应性。     

HXD1型机车自动驾驶系统设计与应用

主要介绍HXD1型交流传动电力机车的自动驾驶系统方案设计与应用情况,以机车改动最小、成本最低为原则,通过加装自动驾驶系统设备、通信及供电线缆实现自动驾驶功能。通过机车自动系统的成功运用,降低了人工劳动强度,杜绝了人为失误因素,提升了机车操纵水平和操纵一致性。     

城市轨道交通全自动运行线路在无人值守模式下的应急处置

基于不同的场景,详细阐述了无人值守模式下全自动运行线路的应急处置方法、区间疏散方式及救援方式。结合专门的自动应急或远程应急功能,使无人值守全自动运行线路应急处置能兼顾运营效率和安全,避免列车迫停区间。建议做好多职能队员的相关培训工作,并合理选择列车自动运行控制系统的应急功能。     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.