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CPS: Breakthrough: A Dynamic Optimization Framework for Connected Automated Vehicles in Urban Environments

CPS：突破：城市环境中联网自动驾驶车辆的动态优化框架

基本信息

批准号：
1645681
负责人：
Christos Cassandras
金额：
$ 42.5万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2022-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1645681&HistoricalAwards=false
关键词：
CPS Breakthrough Dynamic Optimization Framework

项目摘要

Connected Automated Vehicles (CAVs), often referred to as "self-driving cars", will have a profound impact not only on transportation systems, but also in terms of associated economic, environmental, and social effects. As with any such major transformative undertaking, quantifying the magnitude of its expected impact is essential. The first part of this project aims at precisely this quantification (also referred to as the "price of anarchy") by assessing the difference between the performance of a transportation system as it now stands and the performance achievable in a CAV-based environment. A well-designed CAV-based transportation network has the benefit of expanding limited roadway capacity without affecting the existing infrastructure, but rather by seeking novel ways which focus on the vehicles and not the roads. A major part of the proposed project will focus on meeting this goal at the weakest links of a transportation system: the bottleneck points defined by intersections and merging points. The project will use inverse optimization techniques applied to large traffic datasets (from the Eastern Massachusetts road network) to infer unobservable factors, such as user behavior, and use them to construct a predictive model of traffic equilibria. Based on these new traffic demand models, forward optimization problems will be solved which will lead to socially optimal traffic flow equilibria achievable through a CAV-based system. A dynamic optimization framework will also be developed for urban intersections where the motion of CAVs will be controlled based on real-time data communicated over a wireless network to operate both safely and efficiently in a highly dynamic and uncertain environment. Towards this goal, the broader technical challenge of solving dynamic optimization problems on line will be addressed through novel ways that exploit event-driven methodologies with wide applicability in Cyber-Physical Systems. The overall framework will be demonstrated by implementing the key concepts and explicit control and optimization mechanisms in a miniature city test bed with an urban landscape and small mobile robots emulating CAVs with the ability to communicate and share data

互联自动驾驶汽车（CAV），通常被称为“自动驾驶汽车”，不仅将对交通系统产生深远的影响，而且还将对相关的经济、环境和社会影响产生深远的影响。与任何此类重大变革一样，量化其预期影响的大小至关重要。该项目的第一部分旨在通过评估运输系统的性能与CAV环境中可实现的性能之间的差异来精确地量化（也称为“无政府状态的代价”）。一个设计良好的基于CAV的交通网络的好处是在不影响现有基础设施的情况下扩大有限的道路容量，而是通过寻求专注于车辆而不是道路的新方法。拟议项目的一个主要部分将侧重于在交通系统的最薄弱环节实现这一目标：由交叉口和合并点定义的瓶颈点。该项目将使用应用于大型交通数据集（来自东马萨诸塞州公路网）的逆优化技术来推断不可观察的因素，如用户行为，并使用它们来构建交通平衡的预测模型。基于这些新的交通需求模型，前向优化问题将得到解决，这将导致社会最优的交通流平衡，通过基于CAV的系统实现。还将为城市交叉口开发动态优化框架，其中CAV的运动将基于通过无线网络通信的实时数据进行控制，以在高度动态和不确定的环境中安全有效地运行。为了实现这一目标，更广泛的技术挑战在线解决动态优化问题，将通过新的方式，利用事件驱动的方法，在网络物理系统的广泛适用性。通过在一个具有城市景观和小型移动的机器人仿真CAV的微型城市试验台中实现关键概念和显式控制和优化机制，将演示整个框架，该小型机器人具有通信和共享数据的能力