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，提高为纳入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化车辆(CAV)，这种车辆具有战略性地行驶，以减少走走停停的交通。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管理交通的效率。该项目的研究将探索CAV的模拟，以及我们如何改进他们的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。CPS的核心越来越多地是人工智能算法，这些算法可以通过模拟系统在现实世界中的运行方式来编程。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。该项目包括丰田和日产的合作伙伴，他们支持试验台，使研究得以实现，并加速将研究转化为实践。该项目还包括州和地方政府的利益攸关方/合作伙伴，这将有助于在现实世界中进行实验和演示交通拥堵目标以及潜在的减排。在此项目中生成的工具、技术和数据集将作为活动资源共享，以支持在项目生命周期之外的访问。该项目将重点放在本科生研究人员的指导上，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联网。一个激励领域将成为该项目的应用领域是骑士队。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。当车辆和基础设施通信技术加入一组稀疏、协调的拉格朗日控制代理时，车辆和基础设施通信技术对于拥堵管理可能更有用。该项目将使用现有交通传感器和试验床的数据来推动学习和控制开发。一支仪表化和可控制的乘用车车队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试验台进行验证，该试验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智力优势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.