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. If compromised, serious harm to the controlled physical entities and the people operating or utilizing them as well as significant economic losses can result. However, model mismatches between the real system and an imperfect model of the system, in addition to other sources of uncertainties (e.g., measurement errors) disable existing safety and security protection, while robust solutions without learning may be overly conservative. These challenges demonstrate the need to design novel computational tools that can guarantee robust safety and security of cyber-physical systems under real-world uncertainties without sacrificing performance. The project includes research activities that are integrated with education and outreach to engage students and industry partners to appreciate the importance of safety and security for computing-related technologies.

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. Through these mechanisms, at run-time, the systems have a preview of what lies ahead. Leveraging this information to improve performance of CPS while keeping strong guarantees on their safety, therefore, holds great promise for multiple technologies of national interest. We will use driver-assist systems in connected vehicles as the main application. Education and outreach activities will involve undergraduate and graduate students along with stakeholders from local automotive companies.

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. Large-scale development projects have exacerbated urban conditions, making traffic congestion more severe. Additionally, traffic congestion is one of the leading contributors to noise and dust pollution in the city. Altogether, traffic congestion poses major barriers to urban quality of life, but the solutions are complex. There are two main problems with traffic in HCMC. First, HCMC, like other dense urban areas, needs significant financial and technical resources to solve its traffic and infrastructure problems. Second, given that traffic monitoring is carried out by a limited number of staff who watch traffic activities from thousands of camera feeds on multiple screens, there are limits to the number and effectiveness of responses that personnel are able to offer in response to real-time traffic problems.

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. It is the part that allows AI to perceive and understand its surroundings. Humans are very good at understanding what's critical in their environment and the human perceptual system automatically focuses limited cognitive resources on those elements of the scene that matter most, saving a significant amount of ?brain processing power?. Current AI pipelines do not have a similar mechanism, resulting in significantly higher resource costs. The project refactors data analytics and machine intelligence pipelines to allow for better prioritization of external stimuli leveraging and significantly extending advances in scheduling previously developed in the real-time systems research community. The refactored AI pipeline will improve the efficiency and efficacy of AI-enabled systems, allowing them to be safer and more responsive, while at the same time significantly lowering their cost. If successful, the project will help bring machine intelligence solutions to the benefit of all society. This is achieved through interactions between research, education, and outreach, as well as integration of multiple scientific communities, including (i) researchers on embedded computing who offer platforms and schedulers, (ii) researchers on IoT and networking, and (iii) researchers on intelligent applications and application domain experts. The work is an example of cyber-physical computing research, where a new generation of digital algorithms learn to exploit a better understanding of physical systems in order to improve societal outcomes.

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. The outputs of this project will have profound societal impacts on the wellbeing of both healthy individuals and on recovering sick individuals. Research outcomes will enable real time human-built environment interaction to minimize stress and optimize performance in any built environment, and ultimately lead towards economic benefits achieved through wellness and higher productivity. Improved indoor environmental quality in hospital settings will improve patient healing, which is an important societal benefit. Similar strategies can be used for educational facilities, and office buildings. This research encourages Broadening Participation through inclusion of individuals from underrepresented groups (female and Latinx Co-PIs), female and minority students, and a minority serving lead institution from an EPSCoR state. Results will be disseminated broadly through scientific publications and seminars, and K-12 outreach, including STEM competitions, and summer programs.

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. The goal of this research is to design secure RT-CPS that are less complex, easier to analyze, and reliable for critical application domains such as defense, medicine, transportation, manufacturing, and agriculture, to name just a few. Since RT-CPS now permeate most aspects of our daily lives, especially in the smart city and internet-of-things (IoT) context, this research will improve confidence in automated systems by users. Research results will be disseminated to both academia and industry, and permit timely adoption since the hardware required in this research is already publicly available. This project will result in a pipeline of engineers and computer scientists who are well-versed in the interdisciplinary nature of securing RT-CPS, as well as course modules and red-teaming exercises for undergraduate students in all engineering disciplines and interactive learning modules and internship experience for K-12 students in D.C., Detroit, Dallas, and St. Louis.

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

Autonomous systems are subject to multiple regulatory requirements due to their safety-critical nature. In general, it is infeasible to guarantee the satisfaction of all requirements under all conditions. In such situations, the system needs to decide how to prioritize among them. Two main factors complicate this decision. First, the priorities among the conflicting requirements may not be fully established. Second, the decision needs to be made under uncertainties arising from both the learning-based components within the system and the unstructured, unpredictable, and non-cooperating nature of the environments. Therefore, establishing the correctness of autonomous systems requires specification languages that capture the unequal importance of the requirements, quantify the violation of each requirement, and incorporate uncertainties faced by the systems.

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. As a result, understanding the effects of inconsistencies among interacting agents on the overall system is critical for the adoption and acceptance of autonomous systems.

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. As a result, understanding the effects of inconsistencies among interacting agents on the overall system is critical for the adoption and acceptance of autonomous systems.

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. Additionally, the outcomes of this project will allow for the development of tools to assess and improve the effectiveness of different types of preparedness and response policies through feedback loops in the mobile CPS. These feedback loops could show how community behaviors during shocks change when policies are re-defined based on the computations of the CPS, and vice-versa. Previous work by the PI and others has already showed that CPS integrating people and cell phones as sensing platforms can be used to collect location information at large scale and to compute, using data mining and machine learning techniques, human mobility behaviors during shocks. However, most of the results are very limited and ad-hoc, lacking any type of serious applicability from a preparedness and response policy. This project will advance the state of the art by developing accurate methods and effective tools for decision-making during shocks in mobile CPS. From a broader impacts perspective, the proposed research will contribute in two areas: (a) real-world deployments, to promote data-driven policy development, data-driven analyses of human behavior, and the use of feedback loops in mobile CPS for decision-making assessment; and (b) the creation of an educational plan and training opportunities in the areas of data science for social good and mobile CPS for decision making.