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. Specifically, adversaries creating physical-world perceptual illusions may pose a significant threat to the sensing and learning systems of autonomous vehicles, potentially undermining trust in these systems. This research project aims to deepen our understanding of the security and safety risks under physical attacks. The project endeavors to bolster sensing and learning resilience in autonomous driving against malicious perceptual illusion attacks. The success of the project will significantly advance the security and safety of autonomous driving in the face of emerging physical-world threats, paving the way for the safe deployment of autonomous vehicles in next-generation transportation systems.
The goal of this project is to investigate advanced sensing and learning technologies to enhance the precision and robustness of autonomous driving in intricate and hostile environments. The team?s approach includes: (i) a comprehensive framework to evaluate key vulnerabilities in software/hardware components of autonomous driving systems and devise effective attack vectors for generating false and deceptive perceptions; (ii) a real-time super-resolution radar sensing technology and a data fusion approach that integrates features from various sensor types at both the middle and late stages to effectively bolster the robustness of each sensing modality against illusions; and (iii) a systematic framework to enhance the algorithmic generality and achieve robust perception against multi-modal attacks using multi-view representation learning. The presented solutions will undergo rigorous testing using simulations and experiments to validate their effectiveness and robustness. These solutions contribute to the development of more secure and robust autonomous driving systems, capable of withstanding perceptual illusion attacks in real-world scenarios. The project will also offer research training opportunities for underrepresented students across diverse levels and age groups. The resulting novel technology will be shared as open-source for broader dissemination and advancement of the knowledge developed through this project.
This project aims to radically transform traffic management, emergency response, and urban planning practices via predictive analytics on rich data streams from increasingly prevalent instrumented and connected vehicles, infrastructure, and people. Road safety and congestion are a formidable challenge for communities. Current incident management practices are largely reactive in response to road user reports. With the outcome of this project, cities could proactively deploy assets and manage traffic. This would reduce emergency response times, saving lives, and minimizing disruptions to traffic. Efforts are planned in Kindergarten-12 outreach, undergraduate education, outreach to women and minority students, and incorporation of the research into courses, with the goal to inspire and train a diverse cohort for the next-generation of scientists and prepare them for taking on challenges arising from smart and connected communities.
To realize the envisioned system, an integrated research approach is taken to tackle the following closely related research tasks: (1) integration of heterogeneous data streams using a new sparse multi-task multi-view feature fusing method; (2) prediction of traffic incidents by designing a novel high-order low-rank model; (3) teaming of connected vehicles and roadside sensor systems; (4) verification of traffic condition prediction by crowdsourcing the ground truth from user reports in real-time; (5) selection of crowdsourcing participants that recruits and selects voluntary operators of instrumented connected vehicles to provide onboard sensing readings; (6) selection of high quality and diverse images and videos from crowdsourcing vehicles to provide better data for traffic prediction; and 7) design of optimal rerouting strategies to improve commuters' routes in the time of potential traffic disruption.
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.
This project is composed of two interdependent research thrusts, one for investigating adversarial attacks and one for devising countermeasures, aiming to secure the key deep learning-equipped software components of autonomous cyber-physical systems, such as perception, obstacle prediction, and vehicle planning and control. The main deep learning techniques of interest to autonomous cyber-physical systems include convolutional neural networks for detection, recurrent neural networks for prediction, and deep reinforcement learning for control. The technical innovations of the project include ADMM (Alternating Direction Method of Multipliers) based attack generation, concurrent adversarial training and model compression, and multi-sourced defense schemes incorporating adversarial training and ensemble learning. This project will implement and evaluate the proposed attack and defense approaches on real-world prototypes of autonomous cyber-physical systems for autonomous vehicles and unmanned aerial vehicles in the investigators' labs. The investigators will release all the developed models, algorithms, and software to GitHub to facilitate community usage.
Cancer remains the second leading cause of death in the US. Immunotherapy is a cancer treatment that aims to help the body?s immune system fight cancer. While excellent responses have been observed for a large number of patients with varying disease types, a considerably larger number of patients have received little to no benefit from immunotherapy. This varied outcome has been attributed to the highly heterogeneous physical and physiological profile within and around tumors that suppress the immune system?s response. Various physical, chemical, and biological treatment modalities are under investigation for altering the tumor environment from a state where immune effects are suppressed, to one supportive of an anti-tumor immune response. However, these approaches are hampered by the lack of techniques for monitoring the tumor state in response to candidate treatments. Technologies that enable continuous monitoring of the tumor?s immune state, and thereby guide precise delivery of interventions to drive tumors to an immunostimulatory state, offer the promise of unlocking the full potential of immunotherapies. A cyber-physical systems (CPS) perspective is uniquely suited to addressing this challenge, treating the tumor as an ?in body CPS? with the development of sensors and analytical techniques for longitudinal assessment of the tumor, coupled with co-located methods for delivering physical/chemical treatments for modulating the environment within the tumor towards an immunostimulatory state. If successfully developed and translated, the CPS framework for immunomodulation investigated in this project may ultimately guide selection and optimal delivery of priming interventions prior to immunotherapy delivery, determine when priming interventions have successfully modulated the tumor to an immunogenically favorable state, and for assessing treatment response. The investigator team will develop a graduate-level course on biomedical cyber-physical systems along with modules on implantable biomedical sensors for undergraduate courses. Further, this project will provide summer research opportunities for students from under-represented groups via the Pathways to STEM program.
This project will investigate a CPS framework for immunomodulation of the tumor microenvironment (TME), integrating: (1) a unique 3D micro-array sensor and treatment (MIST) device consisting of a sensing/actuation platform for longitudinal sensing and control of physical and physiological parameters within the TME; (2) novel model-informed machine learning techniques for determining tumor immune state from TME physical/physiologic characteristics; and (3) model-guided therapy via the MIST device for driving the TME to an immunostimulatory state. Advanced 3D fabrication technology will provide implantable micromachined multimodal sensing devices to enable longitudinal in vivo sensing of TME parameters such as tissue oxygenation, pH, pressure, and metabolism, and co-located treatment on a single device. Data gathered from implantable sensors will be fused with computational models of biophysical parameters informed by tumor-specific vasculature maps using a graph neural tensor completion approach. The novel hybrid machine learning approach for data imputation and fusion will systematically incorporate uncertainties and provide the basis to infer the immune state of a tumor, validated against gold-standard molecular biomarkers of immune state in experimental small animals. A graph-based clustering approach integrated with a recurrent neural network will be used for the prediction of tumor state changes. Finally, we will evaluate the efficacy of model-guided delivery of energy-based interventions to transform the TME to a pro-immunogenic state and the impact of these interventions on immunotherapy outcomes in small animals.
This project is jointly funded by the Cyber-Physical Program and the Established Program to Stimulate Competitive Research (EPSCoR).
With the advent of ubiquitous connectivity, large-scale data collection, and the remarkable successes of data driven artificial intelligence (AI) in recent years, we have come to a turning point. The time is ripe to consider how advanced data-driven AI technology will impact networked dynamical systems involving both human and machine agents together with physical infrastructure. This symposium will bring together leading experts from academia, government and industry to help identify promising directions in research and education at this nexus. The symposium will comprise invited talks, panel discussions, and brainstorming sessions. Striking the right balance between data-driven innovations and systematic design will be an important part of the conversation.
The outcomes of the symposium will include a meeting report distilled out of the panel discussions and the brainstorming sessions. The report will help inform government and industry stewards / stakeholders about new directions and grand challenges in this important area that will directly impact the nation's competitiveness and security in the coming decades.
The symposium will help identify new research directions at the crossroads of key areas of critical interest to NSF and the nation's research enterprise. NSF's Engineering Directorate has multiple major research programs related to AI and AI-enabled systems. The panel discussions, talks, brainstorming sessions, and the final symposium report will inform NSF and other government stakeholders who sponsor and exploit research and development in these critical areas.
How do we effectively teach both empirical data-driven and ?hard-science? methodology at the same overall number of credit hours? This is one of the key issues that will be discussed at the Symposium, and one of the most important questions we face as we redesign engineering curricula to accommodate the data-driven AI revolution. Bringing together world-class experts to discuss and offer insights on this and other related issues is of tremendous value for the next generation of AI-savvy systems and control engineers. In addition, the organizers will invite select students to participate in the Symposium, paying particular attention to female and URM representation.
The understanding of designing structured surfaces for advanced functionality, such as friction reduction, antifouling, and hydrophobicity, has significantly progressed over the years; however, the critical technical barrier to the application of these structured surfaces is the scalability in manufacturing capability. The biggest challenge in surface patterning is the process scalability, which needs to reconcile the significant scale difference between the individual feature size down to the nano- or micro-level and the large surface-to-be-textured up to the meter level. The project will investigate one vision for future manufacturing -- distributed robotic manufacturing -- to achieve scalable patterning of micro-structured functional surfaces using a cohort of mini-robots. The project will not only push the knowledge boundaries in the scientific understanding of distributed physical intelligence, machine-material interactions, and swarm control, but may open up a new and interdisciplinary research field at the intersection of manufacturing, robotics, control, and cyberphysical systems. Additionally, the project includes outreach at the Museum of Science and Industry in Chicago, open-source software, and curricular innovations, including online classes and training modules. The research will build an educational and outreach platform to enable education and workforce development for STEM educators, next generation workforce, and technical engineers.
The research objectives are to explore and answer three fundamental scientific questions that will enable the vision for future manufacturing in distributed robotic manufacturing. (1) Distributed physical intelligence: a new design framework will be established to distribute the intelligence among mechanical structures, analog circuits, and digital logics, as well as to design unconventional communication channels through both active and passive manners. (2) Unconventional machine-material interaction: new theoretical underpinnings will be established to investigate the machine-material interaction and the possible removal, deformation, and addition of material in this new paradigm, where the tools are extremely flexible and with significantly constrained power. (3) Swarm control: new fundamental knowledge will be generated from novel task decomposition and distribution paradigms to synthesis techniques capable of minimizing control effort, communication, and computation. Instead of top-down control of every individual mini-robot, novel methods will be established through which local rule specifications lead to global distributed pattern completion.
This Future Manufacturing award is supported by the Division of Computer and Network Systems (CNS) of the Directorate for Computer and Information Science and Engineering (CISE), and by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG).
Large-scale systems with societal relevance, such as power generation systems, are increasingly able to leverage new technologies to mitigate their environmental impact, e.g., by harvesting energy from renewable sources. This NSF CPS project aims to investigate methods and computational tools to design a new user-centric paradigm for energy apportionment and distribution and, more broadly, for trustworthy utility services. In this paradigm, distributed networked systems will assist the end users of electricity in scheduling and apportioning their consumption. Further, they will enable local and national utility managers to optimize the use of green energy sources while mitigating the effects of intermittence, promote fairness, equity, and affordability. This project pursues a tractable approach to address the challenges of modeling and designing these large-scale, mixed-autonomy, multi-agent CPSs. The intellectual merits include new scalable methods, algorithms, and tools for the design of distributed decision-making strategies and system architectures that can assist the end users in meeting their goals while guaranteeing compliance with the fairness, reliability, and physical constraints of the design. The broader impacts include enabling the automated design of distributed CPSs that coordinate their decision-making in many applications, from robotic swarms to smart manufacturing and smart cities. The research outcomes will also be used in K-12 and undergraduate STEM outreach efforts.
The proposed framework, termed Automated Synthesis for Trustworthy Autonomous Utility Services (ASTrA), addresses the design challenges via a three-pronged approach. It uses population games to model the effect of distributed decision-making infrastructures (DMI) on large populations of strategic agents. DMIs will be realized via dedicated networked hybrid hardware architectures and algorithms we seek to design. ASTrA further introduces a systematic, layered methodology to automate the design, verification, and validation of DMIs from expressive representations of the requirements. Finally, it offers a set of cutting-edge computational tools to facilitate our methodology by enabling efficient reasoning about the interaction between discrete models, e.g., used to describe complex missions or embedded software components, and continuous models used to describe physical processes. The evaluation plan involves experimentation on a real testbed designed for zero-net-energy applications.
The purpose of this project is to develop new methods to increase the efficiency of freight activity, thereby reducing air pollution and greenhouse gas emissions associated with the freight sector. There are widespread inefficiencies in the freight transport system, many due to lack of coordination across actors in the system: railroads and trucking firms, shipping companies, cargo owners, and port operators. There is a need for a centrally coordinated freight management system that will optimize the flow of freight across the rail and road transportation networks. These networks are very complicated: freight and passengers share the same infrastructure; there is temporal and spatial variation in demand; there are many decision-makers affecting the system. We use optimization and control theory and techniques combined with behavioral models to develop a centrally managed control system. The fundamental concept is efficient freight load balancing, meaning allocating freight demand across time and space to serve the entire set of demands as efficiently as possible. In order to accomplish optimal load balancing, we must address two questions: 1) What is the most efficient and sustainable spatio-temporal allocation of freight shipments across the road network, and 2) what incentives and pricing tools will motivate users to accept this allocation. The second question is necessary, because a system optimization requires changes in behavior for some users relative to each user optimizing independently.
Our research approach is co-Simulation, Control and Optimization with BEhavioral incentives (SCOBE). We use simulation models as part of a closed loop optimization system where the system dynamics and user behavior are monitored and updated. Once we allow for variation in user preferences, conventional approaches to solving the system optimization problem are inadequate. Rather, we must take user preferences directly into account in order to determine the best combination of shipment allocations and user incentives. This project makes the following contributions: First, it advances science by developing a method for system optimization with variation in user preferences. Second, the resulting method will be demonstrated through the participation of at least one trucking company to evaluate its value in a real-world context. Third, particulate emissions from heavy duty trucks are one of the largest sources of human health impacts in urbanized areas. Increasing freight efficiency will reduce particulate and other emissions, as well as reduce greenhouse gas emissions. Fourth, the research will be used to promote under-represented undergraduate students to pursue graduate studies, and promote under-represented high school students to pursue careers in the science, technology, engineering and math (STEM) fields.
Petros A. Ioannou received the B.Sc. degree with First Class Honors from University College, London, England, in 1978 and the M.S. and Ph.D. degrees from the University of Illinois, Urbana, Illinois, in 1980 and 1982, respectively. In 1982, Dr. Ioannou joined the Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, California. He is currently a Professor in the same Department and the Director of the Center of Advanced Transportation Technologies and Associate Director for Research of METRANS, a University Transportation Center. He also holds a courtesy appointment with the Department of Aerospace and Mechanical Engineering and the Department of Industrial Engineering. His research interests are in the areas of adaptive control, neural networks, nonlinear systems, vehicle dynamics and control, intelligent transportation systems and marine transportation. Dr. Ioannou was the recipient of the Outstanding Transactions Paper Award by the IEEE Control System Society in 1984 and the recipient of a 1985 Presidential Young Investigator Award for his research in Adaptive Control. In 2009 he received the IEEE ITSS Outstanding ITS Application Award and the IET Heaviside Medal for Achievement in Control by the Institution of Engineering and Technology (former IEE). In 2012 he received the IEEE ITSS Outstanding ITS Research Award and in 2015 the 2016 IEEE Transportation Technologies Award. Dr. Ioannou is a Fellow of IEEE, Fellow of International Federation of Automatic Control (IFAC), Fellow of the Institution of Engineering and Technology (IET), and the author/co-author of 8 books and over 300 research papers in the area of controls, vehicle dynamics, neural networks, nonlinear dynamical systems and intelligent transportation systems.
This Cyber-Physical Systems (CPS) grant will advance structural health monitoring of concrete structures by relying on data acquired by a novel sensing technology with unprecedented scalability and spatial resolution. Modern society depends critically on sound and steadfast functioning of a variety of engineering structures and infrastructures, such as bridges, buildings, pipelines, geotechnical structures, aircrafts, wind turbines, and industrial facilities. Due to aging, massive urbanization, and climate change, there is a growing need for accurate and reliable assessment of the health condition, performance, and operation of these structures in order to ensure their continuous functioning and safe use. The researched technology enables pervasive and scalable sensing of concrete structures with high resolution by transforming concrete into a smart self-sensing material, thereby enabling reliable long-term structural health monitoring. This in turn contributes to the nation?s sustainability and resilience and to advancing the nation?s prosperity, welfare, and security. The project advances multiple core research areas in structural health monitoring including CPS system architectures using embedded devices, multi-parameter sensing and networking based on radio frequency sensors, and machine learning for accurate and reliable data analytics. The research outcomes are highly translational to various other CPS domains. The project also contributes to secondary education and outreach activities in multiple ways as well as to undergraduate and graduate education.
The aim of this project is to create a novel sensing system comprised of radio frequency sensors that are pervasively embedded in large volumes of concrete structures and that sense their localities using radio frequency properties. The objective is the assessment of key parameters that reflect the behavior of the monitored structure under operational conditions, such as deformation, temperature, and humidity, as well as detection and characterization of damages. The project has the following intellectual contributions: 1) Passive radio frequency-based sensing that operates over a wide range of frequencies; architectures of smart exciters and networked radio frequency sensors that communicate among themselves via backscatter modulation; solar-powered radio frequency exciter platform that powers the sensors. 2) Energy-based sensing and network optimization of the radio frequency sensor network in terms of its monitoring ability and network connectivity given the constraints on the available harvested power at the exciters. 3) Machine learning methods for function estimation based on the principle of ensemble modeling with Gaussian processes and applied to self-localization and to inference of three-dimensional distributions of material parameters within large volumes of concrete structures.
The vision for Advanced Air Mobility (AAM) or formerly Urban Air Mobility (UAM) is to enable an air transportation system that moves people and cargo between places previously underserved by the current aviation market (local, regional, intraregional, urban) using revolutionary new electric vertical take-off and landing (eVTOL) aircraft. AAM has received significant attention from federal agencies. Companies around the globe are competing to build and test eVTOL aircraft to ensure the AAM will become an integral part of people?s daily life. The AAM has enormous economic potential and societal impact, but its success will depend on its ability to scale the operations to the expected high demand with safety guarantee.
This project lays the foundation of safe and scalable learning-based planning and control for autonomous air mobility. Concretely, the project will (i) focus on algorithmic advances of scalable multi-agent aircraft autonomy for real-time separation assurance to increase the airspace capacity; (ii) develop and integrate the online safety guard and offline adaptive stress testing model to provide safety enhancement for the multi-agent aircraft autonomy; (iii) design the collaborative traffic flow planning framework for flight operators and the airspace service provider to improve safety and efficiency when facing demand and capacity uncertainties on the AAM network; and (iv) integrate the developed models and algorithms to build an autonomous AAM ecosystem testbed to perform simulation/flight tests and system level validation. The multidisciplinary approach is based on multi-agent reinforcement learning, safe reinforcement learning, multi-agent stochastic game, and bi-level robust optimization. The proposed effort has transformative impacts to enable safe and scalable advanced air mobility. It could have impact in the way that other CPS tools are designed and implemented to support increasing autonomy and unmanned operations in civil aviation, autonomous cars/trucks, and robotics. The project has an integrated education plan in (i) student innovation competitions, teams and clubs; (ii) interdisciplinary curriculum development and improvement for AI and autonomy in aerospace; (iii) bringing industry experts to students in classroom; and (iv) international student research exchange. The project will engage elected officials and policy makers in AI and machine learning via podcast series, which will provide basic knowledge and insights on legal, ethical, and societal implications of AI. The project will establish a workforce pipeline from high school to postdoc for women in in aerospace via AI and computing.