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

该项目将通过开发从模拟环境中学习的方法，提高为网络物理系统（CPS）构建人工智能算法的能力，CPS将通信技术纳入其中。具体的应用领域是联网和自动驾驶汽车（CAV），它们可以战略性地减少走走停停的交通。与没有通信的车辆相比，采用车辆之间的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自动驾驶汽车的模拟，以及我们如何改进其算法以减少交通拥堵，核心技术的发展将适用于家庭、健康、智能和互联社区。人工智能算法日益成为CPS的核心，可以通过模拟系统在现实世界中的运行方式对其进行编程。一个主要的挑战是建立一个模拟，准确地捕捉系统的复杂性，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转变。该项目还包括州和地方政府的利益相关者/合作伙伴，他们将促进在现实世界中的实验和交通拥堵目标的示范，以及潜在的减排。本项目产生的工具、技术和数据集将作为有效资源共享，以支持在项目生命周期之后的访问。该项目将重点放在对本科生研究人员的指导上，以扩大对计算机的参与。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。一个将成为该项目的应用领域的激励领域是自动驾驶汽车。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自动驾驶汽车。车辆和基础设施通信技术在向一组稀疏的、协调的拉格朗日控制代理提供信息时，对拥堵管理更有用。该项目将使用来自现有交通传感器和试验台的数据来推动学习和控制开发。一组仪表化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真实道路上进行，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。该奖项反映了美国国家科学基金会的法定使命，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。

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.