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. This project aims to advance the state of the art in the Cyber-Physical Systems (CPS) research areas of Autonomy, Safety, and Transportation by ushering in a new CPS paradigm of harmonious and safe integration of highway vehicles with heterogeneous, varying, and mixed human / machine autonomy. Through collaborative research, the project may create new methods and tools to enhance the highway driving safety of heterogeneous vehicles. The outcomes of this work may also be extended to advance other CPS in manufacturing, warehousing, and healthcare applications where interaction among humans and heterogeneous autonomous robots is pervasive and safe coordination among them is critical.

The project seeks to address the emerging challenges associated with vehicles of heterogeneous autonomy in highway transportation by creating a universal framework that can augment AVs1-5 systems to enable safe and harmonious integration of vehicles in highway traffic. The research team will use an interdisciplinary research approach to understand driving behaviors and assess individual perceived safety of other HDVs and AVs1-5, as well as to achieve cooperative, decentralized behavioral coordination and verifiably safe control in highway traffic scenarios. Human-in-the-loop driving simulation experiments and scaled vehicle-traffic system experiments will be conducted to investigate and evaluate the developed methods. Educational activities such as curriculum development and graduate/undergraduate student research participation will be conducted. Research dissemination and K-12 outreach activities will also be pursued to further increase the broader impact of the research.

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. The strategies can adaptively adjust cooperative inference techniques for best utilizing available computation and communication resources and ultimately enable high-accuracy and real-time inference. The project will inspire greater collaborations between experts in wireless communication, edge computing, computer vision, autonomous driving testbed development, and automotive manufacturing, and facilitate AI applications in a variety of IoT systems. The educational testbed developed from this project can be integrated into courses to provide hands-on experiences. This project will benefit undergraduate, master, and Ph.D. programs and increase under-represented groups? engagement by leveraging the existing diversity-related outreach efforts.

A multi-disciplinary team with complementary expertise from Rowan University, Temple University, Stony Brook University, and Kettering University is assembled to pursue a coordinated study of collaborative AI inference. The PIs explore integrative research to enable deep learning technologies in resource-constrained ADAS for high-accuracy and real-time inference. Theory-wise, the PIs plan to take advantage of the observation that DNNs can be decomposed into a set of fine-grained components to allow distributed AI inference on both the vehicle and edge server sides for inference acceleration. Application-wise, the PIs plan to design novel DNN models which are optimized for the cooperative AI inference paradigm. Testbed-wise, a vehicle edge computing platform with V2X communication and edge computing capability will be developed at Kettering University GM Mobility Research Center. The cooperative AI inference system will be implemented, and the research findings will be validated on realistic vehicular edge computing environments thoroughly. The data, software, and educational testbeds developed from this project will be widely disseminated. Domain experts in autonomous driving testbed development, intelligent transportation systems, and automotive manufacturing will be engaged in project-related issues to ensure relevant challenges in this project are impactful for real-world applications.

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. Rather than a one-size-fits-all solution, this grant will provide a sustainable, scalable, and transferrable method for local communities to co-create solutions that meet their needs and align with their values. CHSMs will be community-level hubs where residents can access multiple modes of transport such as shared (e-)bikes, e-scooters, ride hailing, electric vehicle charging stations, and public transit. It will provide more mobility options to residents, increase their access to jobs, ameliorate existing transit deserts, and improve the efficiency of the overall transportation system. The CHSM concept will likely benefit all kinds of communities, with the greatest benefits going to those who are most underserved by the current transportation system and most under-resourced due to the job/housing mismatch. The results and findings from this project will provide new insights into how to improve residents? mobility in vulnerable neighborhoods and alleviate the job/housing mismatch nationwide.

The goal of this project is to develop, implement, and evaluate a Community Hub for Smart Mobility (CHSM) to solve the job/housing mismatch in US cities. The research team will focus on Georgian Acres, a historically under-resourced neighborhood in northeast Austin, Texas and work with multiple civic partners to address the unique transportation needs of the neighborhood. The project will be carried out through the following five thrusts: 1. Co-Designing the GA-CHSM with the Georgian Acres (GA) Neighborhood, 2. Co-Developing the GA-CHSM, 3. Co-Operating the CHSM with Civic Partners, 4. Evaluating the GA-CHSM?s Impacts, and 5. Scaling and transferring the CHSM concept through the City of Austin?s Project Connect. Both quantitative (e.g., GIS analysis, transportation modeling, machine learning) and qualitative (e.g., interviews, surveys, focus groups) research methods will be used in this project to evaluate the success of the CHSM. These findings will contribute to the literatures on transportation planning, community planning, and participatory design and will serve as a model for tackling the job/housing mismatch through community-based mobility solutions.

This project is part of the CIVIC Innovation Challenge which is a collaboration of NSF, Department of Energy Vehicle Technology Office, Department of Homeland Security Science and Technology Directorate and Federal Emergency Management Agency.

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. Embedding smart devices in GI provides numerous benefits to a city, providing insight into the health and effectiveness of the system, and the operations and fitness of the city. However, deploying smart devices in GI is challenging because of the scale and need for long-term deployment, meaning battery powered or expensive plugged-in devices are not feasible. This project builds Smart Green Infrastructure; augmenting GI with battery-free smart devices, powered by energy harvested directly from soil, which gather data, infer, actuate, and collaborate with each other. By harvesting from freely available soil and removing batteries, these devices can last for decades. Through partnerships with organizations in Chicago, Illinois?The Nature Conservancy, the Chicago Botanic Garden, and the Lincoln Park Zoo?the project demonstrates applicability by tackling stormwater management, urban wildlife surveillance, green roofs and other real-world applications. . Beyond cities, the work in this project will enable new applications in agriculture and smart farming, water resources management, and any applications where long term, zero maintenance embedded intelligence is required. Building Smart GI presents cyber-physical systems challenges in enabling robust inference, communication, and coordination on ultra-constrained computing platforms, and despite frequent power failures and dynamic energy availability. These devices harvest energy from soil or plant organic matter using terrestrial Microbial Fuel Cells (MFCs), constructed from inert materials providing decades long lifetime. These devices use machine learning to understand their environment, enabling robust, long-term monitoring that requires no maintenance or replacement. Finally, these devices actuate to "heal" the surrounding environment, switching the MFC from energy generation, to producing a disinfectant instead with the microbial community. A high-powered edge device orchestrates the actions of the swarm of MFC powered nodes, making decisions based on network data and external factors like user tasks. Project tasks include; co-design of the Terrestrial MFC and corresponding energy model for efficient and dynamic energy harvesting and; development of a tiny, resilient computing platform that harvests soil energy and supports sensing, and actuation; creation of a framework for conducting on-device machine learning to recognize subtle environmental changes from lossy data; exploration of orchestration of a network of intermittently powered devices; and conducting a series of end-to-end deployments with Chicago institutions.

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. Hence, this project is to explore the new RL strategies to enable CPS with the capabilities of autonomous learning and generalization to rapidly adapt in unknown situations that were not assumed in the design phase. The results are expected to transform how agents interact in high-dimensional and heterogeneous environment, and therefore could potentially provide in-depth findings for exploring creativity in frontier Artificial Intelligence techniques.

Emerging technologies in communications and vehicle technologies will allow future autonomous vehicles to be platooned together with wireless communications (cyber-connected) or physically forming an actual train (physically-connected). When physically connected, vehicles may dock to and undock from each other en-route when vehicles are still moving. While such platooning can potentially offer substantial societal benefits in safety, mobility and environmental friendliness, their emergence also challenges the classic traffic flow models that do not account for the state that vehicles can have very short to no gaps from each other. And yet, classic traffic flow models are being used for all traffic simulations for assessment on safety, mobility and environment. This project aims to expand classic highway traffic flow models to account for states where vehicles can be very close to or even physically connected with each other. These new models will help stakeholders plan and manage future transportation systems and supply the engineering curriculum with new methods, tools, and experimental platforms oriented towards future smart urban systems.

This Cyber-Physical Systems (CPS) grant will focus on the development of an urban traffic management system, which is driven by public needs for improved safety, mobility, and reliability within metropolitan areas. Future cities will be radically transformed by the Internet of Things (IoT), which will provide ubiquitous connectivity between physical infrastructure, mobile assets, humans, and control systems. In particular, IoT and smart traffic management have the potential to significantly improve increasingly faltering transportation systems that account for over 25% of greenhouse gas emissions and over one trillion dollars of annual economic and social loss. The project develops a hybrid twin that operates in parallel with the real world at real-time resolution, leveraging machine learning and edge computing, to monitor surrounding traffic, send safety warnings to connected vulnerable users, and provide learning-based controls to traffic lights and automated vehicles. As such, the broader impacts include advancing the understanding of urban traffic modeling, computation, and simulation, and enriching transportation science with data science. The accompanying educational plan aims to broaden participation in computing and engineering by underrepresented minorities and women via outreach programs, including programs for Harlem public school teachers and K-12 students, as well as new graduate course development.

Cyber-physical systems such as self-driving cars, drones, and intelligent transportation rely heavily on machine learning techniques for ever-increasing levels of autonomy. In the example of autonomous vehicles, deep learning or deep neural networks can be employed for perception, sensor fusion, prediction, planning, and control tasks. However powerful such machine learning techniques have become, they also expose a new attack surface, which may lead to vulnerability to adversarial attacks and potentially harmful consequences in security- and safety-critical scenarios. This project investigates adversarial machine learning challenges faced by autonomous cyber-physical systems with the aim of formulating defense strategies. The project will collaborate with the Center for STEM (Science, Technology, Engineering and Math) Education at Northeastern University and the Office of Access and Inclusion Center at University of California at Irvine to engage undergraduates, women, and minority students in independent research projects.

The goal of this next phase of the Cyber-Physical Systems Virtual Organization (CPS-VO) is to continue to support and renew the CPS-VO infrastructure, while embracing the data revolution. The project will expand the CPS-VO's capacity to disseminate and collect data, models, and results from existing CPS research projects. This will increase the impact of those projects by making their research more readily accessible to others, as well as providing a means by which issues of repeatability and replicability can be demonstrated. Outreach activities within the research community include workshops to gather research project results, and advanced tutorials that enable CPS researchers to address issues of repeatability and reproducibility. The project will empower the hundreds of authors of tools, experimental validation data sets and education material from the last decade of CPS research to appropriately capture, archive, and make their results broadly accessible from within the Virtual Organization. The project will provide research supervision and mentoring opportunities to undergraduates from underrepresented groups. These undergraduates will be trained in techniques for reproducibility of CPS data results, preparing them for a career in industry or academia. Intellectual Challenges addressed by this new phase of the CPS-VO include: (1) accelerating the pace at which the results of experimental validation of CPS research can be leveraged by other researchers, whether through physical system experiments or design studios; (2) using and replicating active resources and CPS design studios in the virtual era that is embraced by education and research groups; (3) expanding active resources to include data and data analysis resources, empowering CPS researchers to embrace the data revolution; (4) creating highly configurable service bundles that minimize the time investment for publishing tools, design studios, data and models on the CPS-VO platform; and (5) providing use case demonstrations at multiple scales of the CPS-VO modules, accelerating the pace at which adoption of the resources can occur.

Cancers are among the leading causes of death around the world, with an estimated annual mortality of close to 10 million. Despite significant efforts to develop effective cancer diagnosis and therapeutics, the ability to predict patient responses to anti-cancer therapeutic agents remains elusive. This is a critical milestone as getting the right choice of therapy early can mean superior anti-tumor outcomes and increased survival, while the wrong choice means tumor relapse, development of resistance, side effects without the desired benefit, and increased cost of treatment. An cyber-physical system that allows an accurate prediction of patient tumor responses to anti-cancer therapies; that is, enable real-time precision medicine, can have a transformative effect not only on health outcomes, but also on the costs of treatment. The goal of this project is therefore to develop an engineered cyber-physical system that combines advanced biological models with state-of-the-art artificial intelligence methods for predictive, automated screening of anti-cancer drugs and optimizations of their dosing. This will move science towards realizing the long-desired precision medicine paradigm leading to significant social impacts. The project has additional social impacts, including minimizing the exponentially growing ethical issues surrounding the use of animals in the past years through increased adoption of the engineered human cancer and heart tissue model systems. The project will provide opportunities to promote STEM education for K-12 students, train students, especially those from under-represented groups, and disseminate science and engineering knowledge to the public.