CPS: Medium: S2Guard: Building Security and Safety in Autonomous Vehicles via Multi-Layer Protection
Lead PI:
Wenjing Lou
Co-PI:
Abstract

Autonomous vehicles (AVs) are revolutionizing the transportation ecosystem and are expected to become a critical part of our society. AVs are equipped with many electronic devices, including various sensors, electronic control units (ECUs), internal control networks, as well as capabilities in artificial intelligence, computing, storage, and communication.

Performance Period: 10/01/2019 - 09/30/2024
Institution: Virginia Polytechnic Institute and State University
Sponsor: NSF
Award Number: 1837519
CPS: Medium: Collaborative Research: Robust Sensing and Learning for Autonomous Driving Against Perceptual Illusion
Lead PI:
Wenjing Lou
Co-PI:
Abstract

Autonomous driving is on the verge of revolutionizing the transportation system and significantly improving the well-being of people. An autonomous vehicle relies on multiple sensors and AI algorithms to facilitate sensing and perception for navigating the world. As the automotive industry primarily focuses on increasing autonomy levels and enhancing perception performance in mainly benign environments, the security and safety of perception technologies against physical attacks have yet to be thoroughly investigated.

Performance Period: 07/01/2023 - 06/30/2026
Institution: Virginia Polytechnic Institute and State University
Sponsor: NSF
Award Number: 2235232
CPS: Small: Data-Driven Reinforcement Learning Control of Large CPS Networks using Multi-Stage Hierarchical Decompositions
Lead PI:
Wenyuan Tang
Abstract

In the current state-of-the-art machine learning based real-time control of large complex networks such as electric power systems is largely bottlenecked by the curse of dimensionality. Even the simplest control designs demand numerical complexity to accomplish. The problem becomes even more challenging when the network model is unknown, due to which an additional learning time needs to be accommodated.

Performance Period: 01/01/2020 - 12/31/2023
Institution: North Carolina State University
Sponsor: NSF
Award Number: 1931932
CAREER: A Skill-Driven Cooperative Learning Framework for Cyber-Physical Autonomy
Lead PI:
Xiangnan Zhong
Abstract

This project investigates new reinforcement learning (RL) approaches for cyber-physical autonomy to bridge the gap between current intelligent systems and human-level intelligence. The nature of many cyber-physical systems (CPS) is distributed, heterogeneous, and high-dimensional, making the hand-coded functions and task-specific information hard to design in the learning scheme. Large amount of training data is often required for achieving the desired performance, however this limits the generalization to other tasks.

Performance Period: 06/01/2021 - 05/31/2026
Institution: Florida Atlantic University
Sponsor: NSF
Award Number: 2047010
CAREER: Foundations for a Resource-Aware, Cyber-Physical Vehicle Autonomy
Justin Bradley
Lead PI:
Justin Bradley
Abstract

Unmanned Aircraft Systems (UASs), or drones, have tremendous scientific, military, and civilian potential for data collection, monitoring, and interacting with the environment. These activities require high levels of reasoning, perception, and control, and the flexibility to adapt to changing environments. However, like other automated agents, UAS don't possess the ability to refocus their attention or reallocate resources to adapt to new scenarios and adjust performance.

Performance Period: 06/01/2021 - 05/31/2026
Institution: University of Nebraska-Lincoln
Sponsor: National Science Foundation
Award Number: 2047971
Collaborative Research: CPS: Medium: Harmonious and Safe Coordination of Vehicles with Diverse Human / Machine Autonomy
Lead PI:
Junmin Wang
Abstract

Human-driven vehicles (HDVs) and automated vehicles (AVs) of all levels (Level 1-5, AVs1-5) may share the highways in the long and foreseeable future. The increasing vehicle autonomy heterogeneity and diversity may jeopardize the safe and harmonious interaction among such vehicles with mixed autonomy on highways and pose a threat to the safety of all vehicles. This may exacerbate an already growing and alarming national concern on traffic safety.

Performance Period: 07/01/2023 - 06/30/2026
Institution: University of Texas at Austin
Sponsor: National Science Foundation
Award Number: 2312466
Collaborative Research: CPS: Medium: RUI: Cooperative AI Inferencein Vehicular Edge Networks for Advanced Driver-Assistance Systems
Lead PI:
Jungme Park
Co-PI:
Abstract

Artificial Intelligence (AI) has shown superior performance in enhancing driving safety in advanced driver-assistance systems (ADAS). State-of-the-art deep neural networks (DNNs) achieve high accuracy at the expense of increased model complexity, which raises the computation burden of onboard processing units of vehicles for ADAS inference tasks. The primary goal of this project is to develop innovative collaborative AI inference strategies with the emerging edge computing paradigm.

Performance Period: 10/01/2021 - 09/30/2024
Institution: Kettering University
Sponsor: National Science Foundation
Award Number: 2128346
SCC-CIVIC-FA Track A: Co-Creating a Community Hub for Smart Mobility: A University-Government-Nonprofit Partnership
Lead PI:
Junfeng Jiao
Co-PI:
Abstract

This NSF CIVIC grant will provide a sustainable, scalable, and transferable proof of concept for addressing the spatial mismatch between housing affordability and jobs in US cities by co-creating a Community Hub for Smart Mobility (CHSM) in vulnerable neighborhoods with civic partners. The spatial mismatch between housing affordability and jobs causes commuter traffic congestion resulting in an annual $29 billion loss to the U.S. economy alone.

Performance Period: 10/01/2021 - 09/30/2024
Institution: University of Texas at Austin
Sponsor: National Science Foundation
Award Number: 2133302
CPS: Medium: Batteryless Sensors Enabling Smart Green Infrastructure
Lead PI:
Josiah Hester
Co-PI:
Abstract
Cities across the nation have invested significantly in making infrastructure smarter, more sustainable, and more resilient to extreme weather events. Green infrastructure (GI), a general term for planned installations of trees, plants, soils, wetlands, and other natural resource, is increasingly being installed in cities nationwide to provide resilience to flooding, sewer overflows, urban heat, air pollution, habitat loss, and coastal erosion, that are overwhelming traditional infrastructure and negatively impact urban life.
Performance Period: 01/01/2021 - 12/31/2024
Institution: Northwestern University
Sponsor: National Science Foundation
Award Number: 2038853
CAREER: A Skill-Driven Cooperative Learning Framework for Cyber-Physical Autonomy
Lead PI:
Xiangnan Zhong
Abstract

This project investigates new reinforcement learning (RL) approaches for cyber-physical autonomy to bridge the gap between current intelligent systems and human-level intelligence. The nature of many cyber-physical systems (CPS) is distributed, heterogeneous, and high-dimensional, making the hand-coded functions and task-specific information hard to design in the learning scheme. Large amount of training data is often required for achieving the desired performance, however this limits the generalization to other tasks.

Performance Period: 06/01/2021 - 05/31/2026
Institution: Florida Atlantic University
Sponsor: NSF
Award Number: 2047010
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