CPS: TTP Option: Medium: Coordinating Actors via Learning for Lagrangian Systems (CALLS)

CPS：TTP 选项：中：通过拉格朗日系统学习协调参与者 (CALLS)

• 批准号: 2135579
• 负责人: Daniel Work
• 金额: $ 159.89万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135579&HistoricalAwards=false
• 关键词: CPS; TTP; Option; Medium; Coordinating

This project will improve the ability to build artificial intelligence algorithms for Cyber-Physical Systems (CPS) that incorporate communications technologies by developing methods of learning from simulation environments. The specific application area is connected and automated vehicles (CAV) that drive strategically to reduce stop-and-go traffic. Employing communication between vehicles can improve the efficiency of vehicle control systems to manage traffic compared to vehicles without communication. The research of this project will explore the simulation of CAVs and how we can improve their algorithms to reduce traffic congestion, with core technology developments that are applicable to homes, health, and smart and connected communities. Increasingly at the heart of CPS are artificial intelligence algorithms, which can be programmed using a simulation of how the system should operate in the real world. A major challenge is building a simulation that accurately captures the complexity of the system in question, and how it can be controlled. The project includes partners from Toyota and Nissan that support testbeds enabling the research and accelerate transition of research to practice. The project aslo includes state and local Government stakeholders / partners which will facilitate experimentation in the real-world and demonstration of traffic congestion objectives as well as potentially emission reduction. Tools, technologies, and datasets generated in this project will be shared as active resources to support access beyond the life of the project. The project brings a focus on mentorship for undergraduate researchers, in order to broaden participation in computing. This project will develop new reinforcement learning approaches for Lagrangian control that accommodate communication and networking between actuators. A motivating domain that will be an application area of the project is CAVs. A major challenge is leveraging a small number of CAVs before those technologies realize full adoption rates. Vehicle and infrastructure communication technologies can be more useful for congestion management when feeding into a group of sparse, coordinated Lagrangian control agents. The project will use data from existing traffic sensors and testbeds to drive learning and control development. A fleet of instrumented and controllable passenger vehicles will be used for data collection and actuation. Validation experiments will be conducted using these vehicles on live roadways, and the results will be validated using a camera-based testbed that collects detailed traffic data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将提高为网络物理系统（CPS）构建人工智能算法的能力，该算法通过从模拟环境中开发学习方法来结合通信技术。连接特定的应用区域并自动化车辆（CAV），以策略性地驱动以减少停车场的交通。与没有通信的车辆相比，使用车辆之间的通信可以提高车辆控制系统的效率管理交通。该项目的研究将探讨骑士的模拟以及我们如何改善其算法以减少交通拥堵的方式，并使用适用于家庭，健康以及智能和互联社区的核心技术发展。 CP的核心越来越多的是人工智能算法，可以通过模拟该系统在现实世界中的运行方式进行编程。一个主要的挑战是构建一个模拟，以准确捕获有关系统的复杂性以及如何控制系统的复杂性。该项目包括来自丰田和Nissan的合作伙伴，这些合作伙伴支持测试床，以实现研究并加速研究过渡到实践。 ASLO项目包括州和地方政府的利益相关者 /合作伙伴，这些伙伴将促进现实世界中的实验，并证明交通拥堵目标以及潜在的减排。 该项目中生成的工具，技术和数据集将作为主动资源共享，以支持超出项目寿命的访问。该项目将专注于本科研究人员的指导，以扩大参与计算。 该项目将开发新的强化学习方法，用于拉格朗日控制，以适应执行器之间的沟通和网络。一个将成为该项目应用领域的激励领域是骑士。一个主要的挑战是在这些技术实现完全采用率之前利用少数骑士。当进食一组稀疏的，协调的拉格朗日控制剂时，车辆和基础设施通信技术对于拥堵管理可能更有用。该项目将使用现有流量传感器和测试床的数据来推动学习和控制开发。一组仪器和可控的乘用车将用于数据收集和驱动。验证实验将在实时道路上使用这些车辆进行，并将使用基于摄像机的测试台进行验证，该测试台收集详细的交通数据。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来获得支持。

项目成果

CAV Traffic Control to Mitigate the Impact of Congestion from Bottlenecks: A Linear Quadratic Regulator Approach and Microsimulation Study

• DOI: 10.1145/3636464
• 发表时间: 2023-06
• 期刊: Journal on Autonomous Transportation Systems
• 作者: Suyash C. Vishnoi;Junyi Ji;MirSaleh Bahavarnia;Yuhang Zhang;A. Taha;C. Claudel;D. Work

I-24 MOTION: An instrument for freeway traffic science

I-24 MOTION：高速公路交通科学仪器

• DOI: 10.1016/j.trc.2023.104311
• 发表时间: 2023
• 期刊: Transportation Research Part C: Emerging Technologies
• 作者: Gloudemans, Derek;Wang, Yanbing;Ji, Junyi;Zachár, Gergely;Barbour, William;Hall, Eric;Cebelak, Meredith;Smith, Lee;Work, Daniel B.
• 通讯作者: Work, Daniel B.

Parameter Estimation for Decoding Sensor Signals

解码传感器信号的参数估计

• DOI: 10.1145/3576841.3589622
• 发表时间: 2023
• 期刊: Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems
• 作者: Nice, Matthew;Bunting, Matthew;Zachar, Gergely;Bhadani, Rahul;Ngo, Paul;Lee, Jonathan;Bayen, Alexandre;Work, Dan;Sprinkle, Jonathan
• 通讯作者: Sprinkle, Jonathan

Experimental testing of a control barrier function on an automated vehicle in live multi-lane traffic

实时多车道交通中自动车辆控制屏障功能的实验测试

• DOI: 10.1109/di-cps56137.2022.00011
• 发表时间: 2022
• 期刊: 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS
• 作者: Gunter, George;Nice, Matthew;Bunting, Matt;Sprinkle, Jonathan;Work, Daniel B.
• 通讯作者: Work, Daniel B.

Data from the Development Evolution of a Vehicle for Custom Control

定制控制车辆的开发演变数据

• DOI: 10.1109/di-cps56137.2022.00013
• 发表时间: 2022
• 期刊: 2022 2nd Workshop on Data-Driven and Intelligent Cyber-Physical Systems for Smart Cities Workshop (DI-CPS
• 作者: Bunting, Matt;Bhadani, Rahul;Nice, Matt;Elmadani, Safwan;Sprinkle, Jonathan
• 通讯作者: Sprinkle, Jonathan