CAREER: Towards Non-Conservative Learning-Aided Robustness for Cyber-Physical Safety and Security
Lead PI:
Sze Zheng Yong
Abstract

The goal of this project is to provide a scientific basis to understand and leverage the interaction among physical systems, artificial intelligence/cyber-human agents and their environment through the development of control synthesis tools to reason about safety and security under real-world uncertainties. Such cyber-physical systems, which include many vital infrastructures that sustain modern society (e.g., transportation systems, electric power distribution) are usually safety-critical.

Performance Period: 10/01/2022 - 04/30/2025
Institution: Northeastern University
Sponsor: NSF
Award Number: 2313814
CPS: Medium: Collaborative Research: Data-Driven Modeling and Preview-Based Control for Cyber-Physical System Safety
Lead PI:
Sze Zheng Yong
Abstract

This project will develop the theory and algorithmic tools for the design of provably-safe controllers that can leverage preview information from different sources. Many autonomous or semi-autonomous cyber-physical systems (CPS) are equipped with mechanisms that provide a window of projecting into the future. These mechanisms can be forward looking sensors like cameras (and corresponding perception algorithms), map information, forecast information, or more complicated predictive models of external agents learned from data.

Performance Period: 10/01/2022 - 12/31/2023
Institution: Northeastern University
Sponsor: NSF
Award Number: 2312007
EAGER: Crowd-AI Sensing Based Traffic Analysis for Ho Chi Minh City Planning Simulation
Lead PI:
Tam Nguyen
Co-PI:
Abstract

This activity is in response to NSF Dear Colleague Letter Supporting Transition of Research into Cities through the US ASEAN (Association of Southeast Asian Nations Cities) Smart Cities Partnership in collaboration with NSF and the US State Department. Ho Chi Minh City (HCMC), an ASEAN city in Vietnam, is well-known for its traffic congestion and high density of vehicles, cars, buses, trucks, and a swarm of motorbikes (7.3 million motorbikes for more than 8.4 million residents) that overwhelm city streets.

Performance Period: 08/01/2020 - 07/31/2024
Institution: University of Dayton
Sponsor: NSF
Award Number: 2025234
Collaborative Research: CPS: Medium: Real-time Criticality-Aware Neural Networks for Mission-critical Cyber-Physical Systems
Tarek Abdelzaher
Lead PI:
Tarek Abdelzaher
Abstract

Advances in artificial intelligence (AI) make it clear that intelligent systems will account for the next leap in scientific progress to enable a myriad of future applications that improve the quality of life, contribute to the economy, and enhance societal resilience to a broad spectrum of disruptions. Yet, advances in AI come at a considerable resource costs. To reduce the cost of AI, this project takes inspiration from biological systems. It is well-known that a key bottleneck in AI is the perception subsystem.

Performance Period: 07/15/2021 - 06/30/2024
Institution: University of Illinois at Urbana-Champaign
Sponsor: NSF
Award Number: 2038817
Collaborative Research: CPS: TTP Option: Medium: i-HEAR: immersive Human-On-the-Loop Environmental Adaptation for Stress Reduction
Teresa Wu
Lead PI:
Teresa Wu
Abstract

There is no question that indoor environments are often uncomfortable or unhealthy for occupants. This is an even more critical issue in healthcare facilities, where patients may experience the stressful effects of poor thermal, luminous, and acoustic environments more acutely. With complementary expertise from engineering and psychology, the proposed research is focused on creating a human-on-the-loop, responsive indoor environmental system with the potential to offer better quality of care in hospitals.

Performance Period: 10/01/2021 - 09/30/2024
Institution: Arizona State University
Sponsor: NSF
Award Number: 2038905
Collaborative Research: CPS: Medium: Timeliness vs. Trustworthiness: Balancing Predictability and Security in Time-Sensitive CPS Design
Lead PI:
Tam Chantem
Co-PI:
Abstract

Many cyber-physical systems (CPS) have real-time (RT) requirements. For these RT-CPS, such as a network of unmanned aerial vehicles that deliver packages to customers? homes or a robot that performs/aides in cardiac surgery, deadline misses may result in economic losses or even fatal consequences. At the same time, as these RT-CPS interact with, and are depended on by, humans, they must also be trustworthy.

Performance Period: 02/01/2021 - 01/31/2026
Institution: Virginia Polytechnic Institute and State University
Sponsor: NSF
Award Number: 2038726
CAREER: Establishing correctness of learning-enabled autonomous systems with conflicting requirements
Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Performance Period: 02/15/2022 - 01/31/2027
Institution: Iowa State University
Sponsor: NSF
Award Number: 2141153
Collaborative Research: CPS: Medium: Sharing the World with Autonomous Systems: What Goes Wrong and How to Fix It
Abstract

As autonomous systems start to operate in open, uncontrolled environments alongside humans, safety becomes a major concern. In applications in which human-operated systems and autonomous systems are in close interaction, the heterogeneity causes different agents to exhibit different behaviors under the same situation due to the differences in how they see the world and make decisions. For example, autonomous vehicles tend to be more conservative than average human drivers, leading to instances of confusion and frustration of human drivers when encountering an autonomous vehicle.

Performance Period: 06/15/2022 - 05/31/2025
Institution: Iowa State University
Sponsor: NSF
Award Number: 2211141
Collaborative Research: CPS: Medium: Sharing the World with Autonomous Systems: What Goes Wrong and How to Fix It
Lead PI:
Ufuk Topcu
Abstract

As autonomous systems start to operate in open, uncontrolled environments alongside humans, safety becomes a major concern. In applications in which human-operated systems and autonomous systems are in close interaction, the heterogeneity causes different agents to exhibit different behaviors under the same situation due to the differences in how they see the world and make decisions. For example, autonomous vehicles tend to be more conservative than average human drivers, leading to instances of confusion and frustration of human drivers when encountering an autonomous vehicle.

Performance Period: 06/15/2022 - 05/31/2025
Institution: University of Texas at Austin
Sponsor: NSF
Award Number: 2211432
CAREER: Data-driven Models of Human Mobility and Resilience for Decision Making
Abstract

This project envisions mobile cyber-physical systems (CPS) where people carrying cell phones generate large amounts of location information that is used to sense, compute and monitor human interactions with the physical environment during environmental dislocations. The main objective will be to identify the types of reactions populations have to a given type of shock, providing decision makers with accurate and informative data-driven representations they can use to create preparedness and response plans.

Performance Period: 04/01/2018 - 03/31/2024
Institution: University of Maryland College Park
Sponsor: NSF
Award Number: 1750102
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