Siting charging stations for electric vehicle adoption in shared autonomous fleets

The transportation sector is undergoing three revolutions: shared mobility, autonomous driving, and electrification. When planning the charging infrastructure for electric vehicles, it is critical to consider the potential interactions and synergies among these three emerging systems. This study proposes a framework to optimize charging infrastructure development for increasing electric vehicle (EV) adoption in systems with different levels of autonomous vehicle adoption and ride sharing participation. The proposed model also accounts for the pre-existing charging infrastructure, vehicle queuing at the charging stations, and the trade-offs between building new charging stations and expanding existing ones with more charging ports.Using New York City (NYC) taxis as a case study, we evaluated the optimum charging station configurations for three EV adoption pathways. The pathways include EV adoption in a 1) traditional fleet (non-autonomous vehicles without ride sharing), 2) future fleet (fully autonomous vehicles with ride sharing), and 3) switch-over from traditional to future fleet. Our results show that, EV adoption in a traditional fleet requires charging infrastructure with fewer stations that each has more charging ports, compared to the future fleet which benefits from having more scattered charging stations. Charging will only reduce the service level by 2% for a future fleet with 100% EV adoption. EV adoption can reduce CO₂ emissions of NYC taxis by up to 861 Tones/day for the future fleet and 1100 Tones/day for the traditional fleet.     

一种新的鱼雷垂直命中目标的制导方法

该文针对鱼雷采用前视角声纳和旁视声纳识别和跟踪目标,引入了初始导引段跟踪虚拟目标的概念,提出一种新的精确制导方法,给出了导引跟踪和垂直命中弹道的数学模型。经仿真和分析表明：该制导方法对攻击目标的初始态势几乎没有限制,即使目标为逃避攻击进行转向机动,也能垂直命中目标。     

考虑合乘的共享自动驾驶汽车选择行为实证分析

共享自动驾驶汽车(Shared Autonomous Vehicles,SAV)是自动驾驶汽车和共享经济相结合的产物,为人们提供了一种新型的出行方式. 为探究出行者在考虑合乘的SAV与私家车或公共交通之间的选择偏好,实施了SAV选择意愿调查,并分析了考虑合乘的SAV的潜在用户特征. 基于问卷调查所得有效数据,采用K-Means 聚类法划分了历史出行模式,利用因子分析对性格态度特征进行了分类. 分别对有无私家车人群建立解释变量的参数服从不同分布的混合Logit 模型,并对参数标定结果进行对比分析. 研究结果表明,出行方式特性非常显著地影响出行者方式选择行为,性格态度特征是影响出行者选择考虑合乘的SAV出行方式的显著因素,且其显著性明显高于性别、年龄等社会经济属性.     

Development of a signal-head-free intersection control logic in a fully connected and autonomous vehicle environment

Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-head-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 59.4% and 83.7% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.     

Evaluation of a transportation system employing autonomous vehicles

The problem of studying public transportation systems with autonomous vehicles is challenging because of behavioral differences that make existing models poorly fit and the technical difficulties involved in studying large autonomous systems operating on a grand scale. In this paper, we propose the following: (i) an autonomous transportation network setting; (ii) a method for modeling autonomous vehicles in simulation; and (iii) a high‐performance simulation platform that allows analysis and visualization of transportation technologies. Results from microsimulation confirm theoretical benefits and improvements from employing autonomous systems in an example setting and highlight the platform's general ability to allow researchers to implement novel transportation systems and study the cost benefit variations occurring between them. Copyright © 2017 John Wiley & Sons, Ltd.     

自动驾驶对交通运输系统规划的影响综述

车辆是交通运输系统的重要组成部分,伴随着自动驾驶技术的发展与应用,交通运输系统 将发生深刻变革。本文聚焦于自动驾驶技术对交通运输系统规划的影响,综述自动驾驶特点下 交通数据采集与管理手段、土地利用、停车需求、交通供需、交通需求预测、交通网络布局等方面 发生的新变化。在此基础上,从规划的角度出发,实现对自动驾驶环境下交通运输系统的再认 知,总结自动驾驶环境下交通需求预测、城市交通网络布局等新的交通规划方法与技术。通过对 交通运输系统的再认知发现：在自动驾驶环境下,交通数据具有细粒度、高鲜度的新特点;土地利 用模式将发生改变,城市将呈扩张和去工业化趋势,停车需求减小;交通系统供给能力和可靠性 提高,出行需求的时空分布将更为分散。交通系统规划方法的变化体现在交通需求预测和交通 网络布局两个层面：交通需求预测框架从“四步”框架转变为模型组合化和出行行为一体化的预 测框架,同时,需求预测的各阶段需引入对自动驾驶特征及其系统性影响的分析;交通网络布局 设计采用连续时域上的布局设计框架,有望解决传统交通网络布局设计的时滞性问题,可适应并 服务于动态变化的土地利用及交通需求。本研究认为,未来需重点研究自动驾驶对交通安全、交 通拥堵、公共交通规划、慢行交通规划等方面的影响。此外,解决自动驾驶实测数据缺乏的困境、 解析异构交通阶段交通系统的运作机理、应对交通需求反弹引起的供不应求、评估难以衡量的外 部成本等问题将是未来研究的难点。     

基于开放式网络环境的自主信任建立模型

在开放式网络环境中,信任是进行信息安全交互的前提和基础。本文首先分析开放式网络环境的有关信任管理问题,讨论开放式网络环境有关信任建立的问题;以图论为基础,定义了基于本地信息交互的信任建立规则,提出基于开放式网络环境自主信任建立模型,主要研究开放式网络环境中各主体如何只依靠本地信息来自主建立信任,具体提出了一种自主信任建立策略,分析表明它能保证各实体信任的自主建立并最终达到稳态;由于信任建立过程的动态性,最后讨论网络拓扑结构对信任传递的影响,并以小世界网络模型(-model)为实验环境分析讨论自主信任建立的速度问题,进一步验证自主信任建立模型的可行性和合理性。为网络管理的研究提供一个有价值的思路。     

多水下机器人编队通信研究

针对多水下机器人编队控制中的水下无线通信问题,提出了一种基于调度的水下无线环状媒介访问控制协议,克服了由于多跳带来的隐藏终端和暴露终端的问题,保证了系统通信的公平接入和周期性控制,同时通过采用数据包捎带技术,在数据包中携带令牌信息,减少额外发送令牌控制包等方法来提高系统吞吐量。N S仿真软件上的仿真实验分析了新协议的性能,通过与类似ALOHA的基于竞争协议的对比实验确定新协议的使用范围,验证了上述媒介访问控制协议的有效性和可行性。     

NTSM控制的AUV路径跟踪控制研究

针对非线性欠驱动自治水下机器人（Autonomous underwater vehicle，缩写为AUV），提出了一种基于非奇异终端滑模（Non—singular terminal sliding mode，缩写为NTSM）控制的鲁棒路径跟踪控制方法。在跟踪控制系统中，采用的参考变量为非时间量，摆脱了时间因素的影响，有利于提高AUV在不确定环境中的跟踪能力。应用指数趋近律进行NTSM控制器设计，能保证系统状态在有限时间内到达平衡点。数值仿真结果验证了该控制律的路径跟踪效能。     

Study of Fuzzy Neural Networks Model for System Condition Monitoring of AUV

A structure equivalent model of fuzzy-neural networks for system condition monitoring is proposed, whose outputs are the condition or the degree of fault occurring in some parts of the system. This network is composed of six layers of neurons, which represent the membership functions, fuzzy rules and outputs respectively. The structure parameters and weights are obtained by processing off-line learning, and the fuzzy rules are derived from the experience. The results of the computer simulation for the autonomous underwater vehicle condition monitoring based on this fuzzy-neural networks show that the network is efficient and feasible in gaining the condition information or the degree of fault of the two main propellers.