Matthew Nice

Wed, 01 Jan 2025 00:00:00 +0000

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

Fri, 01 Oct 2021 00:00:00 +0000

This project improves 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 explores 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.

The project includes state and local Government stakeholders and partners which 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 are 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 develops new reinforcement learning approaches for Lagrangian control that accommodate communication and networking between actuators. A motivating domain that is 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. Validation experiments are conducted using these vehicles on live roadways, and the results are validated using a camera-based testbed that collects detailed traffic data.

This is collaborative research with:

Dan Work (PI, Vanderbilt University)
Jonathan Sprinkle (Vanderbilt University)

This work is supported by the National Science Foundation under award . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

CNS-2135579 | Compositional Systems Lab

Matthew Nice

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