CAREER: Correct-By-Design Control of Traffic Flow Networks

职业：交通流网络的正确设计控制

• 批准号: 1749357
• 负责人: Samuel Coogan
• 金额: $ 50.01万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749357&HistoricalAwards=false
• 关键词: CAREER; Correct; Design; Control; Traffic

Modern cities accommodate more people than ever before, leading to transportation networks that operate at or near capacity. In addition, the next generation of transportation systems will include connected vehicles, connected infrastructure, and increased automation, and these advances must coexist with legacy technology into the foreseeable future. Accommodating these rapidly developing advancements requires smarter and more efficient use of existing infrastructure with guarantees of performance, safety, and interoperability. The goal of this project is to develop fundamental theory and domain-driven techniques for controlling traffic flow in large-scale transportation networks. Recent advances in inexpensive sensors, wireless technology, and the Internet of Things (IoT) enable real-time connectivity of vehicles and infrastructure that offers abundant data and unprecedented opportunities for efficient and optimized transportation systems. The main technical goal is to develop techniques and algorithms that are correct-by-design, ensuring that these transportation systems satisfy required operating specifications. In pursuit of this goal, the project will first develop models of traffic flow from rich data streams and then will leverage these models to enable scalable control approaches. In addition, this project will integrate an ambitious education plan that includes a redesigned introductory course in control theory for undergraduates. The course will be restructured to focus on modern challenges in control, culminating in a Control Grand Challenge design competition in which students will design a controller for an autonomous, scale-model car and then compete with their design. To achieve systems that satisfy the rich design specifications demanded of traffic networks, the project will especially focus on bringing powerful techniques from formal methods for verification and synthesis to large-scale physical networks. These formal methods were originally developed for specifying and verifying the correct behavior of software and hardware systems, and an important research objective now is to ensure these approaches are scalable, adaptable, and reliable when applied to physical control systems. The project will focus on the following objectives: i) Develop theory and models for the dynamic behavior of traffic networks that captures domain-specific phenomena such as congestion propagation, ii) Determine how traffic flow dynamics will change as vehicles are increasingly equipped with autonomous capabilities, iii) Identify and exploit intrinsic structure in traffic flow networks to enable scalable formal methods for verification and synthesis, and iv) Use data available through industry collaborations to develop probabilistically correct control of traffic flow networks. These objectives address a growing need for systematic guarantees of performance in traffic networks as the increasing complexity and interdependence of transportation systems renders ad hoc approaches insufficient. The research activities of this project will use real traffic data available through ongoing collaborations with industry. An expected outcome of this project is a suite of scalable algorithms that will be tested on a pilot traffic network available through this collaboration. In addition, the project will establish foundational theory applicable outside the traffic domain.

现代城市容纳的人口比以往任何时候都多，导致交通网络达到或接近满负荷运行。此外，下一代交通系统将包括联网车辆、联网基础设施和更高的自动化程度，在可预见的未来，这些进步必须与传统技术共存。要实现这些快速发展的进步，需要更智能、更高效地使用现有基础设施，并保证性能、安全性和互操作性。 该项目的目标是开发用于控制大规模交通网络中的交通流的基础理论和领域驱动技术。廉价传感器、无线技术和物联网（IoT）的最新进展实现了车辆和基础设施的实时连接，从而提供了丰富的数据和前所未有的机会， 优化运输系统。 主要的技术目标是开发设计正确的技术和算法，确保这些运输系统满足所需的操作规范。为了实现这一目标，该项目将首先从丰富的数据流中开发交通流模型，然后利用这些模型实现可扩展的控制方法。此外，该项目将整合一个雄心勃勃的教育计划，其中包括重新设计的本科生控制理论入门课程。该课程将进行重组，重点关注控制方面的现代挑战，最终将举办控制大挑战设计竞赛，学生将为自动驾驶的比例模型汽车设计控制器，然后与他们的设计进行竞争。为了实现满足交通网络丰富设计规范要求的系统，该项目将特别关注将验证和综合的形式化方法的强大技术引入大规模物理网络。这些形式化方法最初是为了指定和验证软件和硬件系统的正确行为而开发的，现在的一个重要研究目标是确保这些方法在应用于物理控制系统时具有可扩展性，适应性和可靠性。该项目将侧重于以下目标：i）开发交通网络动态行为的理论和模型，捕获特定领域的现象，如拥堵传播，ii）确定交通流动态将如何随着车辆越来越多地配备自主能力而变化，iii）识别和利用交通流网络中的内在结构，以实现可扩展的形式化方法进行验证和合成，以及iv）使用通过行业协作可获得的数据来开发交通流网络的概率正确控制。由于运输系统日益复杂和相互依存，采取临时办法是不够的，因此，这些目标针对的是日益增长的对系统保证交通网络性能的需要。该项目的研究活动将使用通过与工业界的持续合作获得的真实的交通数据。该项目的预期成果是一套可扩展的算法，将通过这种合作在试点交通网络上进行测试。此外，该项目将建立适用于交通领域以外的基础理论。

项目成果

Interval Signal Temporal Logic From Natural Inclusion Functions

自然包含函数的区间信号时态逻辑

• DOI: 10.1109/lcsys.2023.3337744
• 发表时间: 2023
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Baird, Luke;Harapanahalli, Akash;Coogan, Samuel
• 通讯作者: Coogan, Samuel

Continuous Reachability Task Transition Using Control Barrier Functions

使用控制屏障函数的连续可达性任务转换

• 发表时间: 2020
• 期刊: IFAC-PapersOnLine
• 作者: Srinivasan, Mohit;Santoyo, Cesar;Coogan, Samuel
• 通讯作者: Coogan, Samuel

Control of Mobile Robots Using Barrier Functions Under Temporal Logic Specifications

• DOI: 10.1109/tro.2020.3031254
• 发表时间: 2021-04-01
• 期刊: IEEE TRANSACTIONS ON ROBOTICS
• 影响因子: 7.8
• 作者: Srinivasan, Mohit;Coogan, Samuel
• 通讯作者: Coogan, Samuel

Efficient Learning of Hyperrectangular Invariant Sets Using Gaussian Processes

使用高斯过程有效学习超矩形不变集

• DOI: 10.1109/ojcsys.2022.3206083
• 发表时间: 2022
• 期刊: IEEE Open Journal of Control Systems
• 作者: Cao, Michael Enqi;Bloch, Matthieu;Coogan, Samuel
• 通讯作者: Coogan, Samuel

Mixed Monotonicity for Reachability and Safety in Dynamical Systems

动态系统中可达性和安全性的混合单调性

• 发表时间: 2020
• 期刊: IEEE Conference on Decision and Control
• 作者: Coogan, Samuel