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.

该项目将通过开发从模拟环境中学习的方法，提高为结合通信技术的网络物理系统（CPS）构建人工智能算法的能力。具体的应用领域是互联和自动驾驶车辆（CAV），这些车辆可以战略性地驾驶，以减少走走停停的交通。与没有通信的车辆相比，在车辆之间采用通信可以提高车辆控制系统管理交通的效率。该项目的研究将探索CAV的模拟，以及如何改进其算法以减少交通拥堵，核心技术开发适用于家庭，健康以及智能和互联社区。人工智能算法越来越成为CPS的核心，可以使用系统在真实的世界中如何操作的模拟来编程。一个主要的挑战是建立一个模拟，准确地捕捉问题系统的复杂性，以及如何控制它。该项目包括来自丰田和日产的合作伙伴，他们支持测试平台，使研究能够加速研究向实践的过渡。该项目还包括州和地方政府的利益相关者/合作伙伴，这将有助于在现实世界中进行实验，并展示交通拥堵目标以及潜在的减排。 本项目中生成的工具、技术和数据集将作为活动资源共享，以支持项目生命周期之后的访问。该项目的重点是为本科研究人员提供指导，以扩大对计算的参与。 该项目将为拉格朗日控制开发新的强化学习方法，以适应执行器之间的通信和网络。一个激励领域，将是该项目的应用领域是CAV。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量的CAV。车辆和基础设施通信技术可以更有用的拥塞管理时，输入到一组稀疏的，协调的拉格朗日控制代理。该项目将使用现有交通传感器和测试平台的数据来驱动学习和控制开发。一队装有仪表和可控的乘用车将用于数据收集和驱动。验证实验将使用这些车辆在现场道路上进行，结果将使用基于摄像头的测试平台进行验证，该测试平台收集详细的交通数据。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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