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

互联自动化汽车(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，而且将在相关的经济、环境和社会影响方面产生深远的影响。与任何这样的重大变革事业一样，量化其预期影响的程度至关重要。该项目第一部分的目的正是为了量化(也称为“无政府状态的代价”)，评估现有运输系统的性能与在基于CAV的环境中可实现的性能之间的差异。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。拟议项目的主要部分将侧重于在运输系统最薄弱的环节实现这一目标：由交叉口和汇合点确定的瓶颈点。该项目将使用应用于大型交通数据集(来自马萨诸塞州东部公路网)的反向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。在这些新的交通需求模型的基础上，将解决正向优化问题，从而通过基于CAV的系统实现社会最优交通流均衡。还将为城市交叉口开发一个动态优化框架，其中CAV的运动将基于通过无线网络传输的实时数据进行控制，以便在高度动态和不确定的环境中安全高效地运行。为了实现这一目标，将通过利用在网络物理系统中具有广泛适用性的事件驱动方法的新颖方法来解决在线解决动态优化问题的更广泛的技术挑战。整个框架将通过在微型城市试验台上实施关键概念和显式控制和优化机制来演示，该试验台带有城市景观和模拟具有通信和共享数据能力的小型移动机器人

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