CPS: TTP Option: Medium: Discovering and Resolving Anomalies in Smart Cities

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Understanding complex activity due to humans and vehicles in a large environment like a city neighborhood or even an entire city is one of the main goals of smart cities. The activities are heterogeneous, distributed, vary over time and mutually interact in many ways, making them hard to capture and understand and mitigate issues in a timely manner. While there has been tremendous progress in capturing aggregate statistics that helps in traffic and city management as well as personal planning and scheduling, much of this work ignores anomalous patterns. Examples include protests, erratic driving, near accidents, construction zone activity, and numerous others. Discovering and resolving anomalies is challenging for many reasons as they are complex and rare, depend on the context and depend on the spatial and temporal extent over which they are observed. There are potentially a large number of anomalies or anomalous patterns, so they are impossible to label and describe manually. The PIs will conduct research to address automatic discovery and resolution of anomalous patterns in smart city visual data. The PIs will leverage the large amount of visual data they have access to ranging from cameras at many intersections in Pittsburgh, around the Carnegie Mellon University neighborhood, cameras installed on public buses, and physical distribution networks in the city. The project will include the following four closely integrated research thrusts: (1) Extracting anomalies in the presence of noise due to visual processing algorithms, (2) Automatically discover anomalies at different spatial and temporal scales with intelligent coordinated and distributed planning, (3) discovering the relationship with anomalies and context, and (4) Resolving Anomalies through Hard and Soft Actuation using both automatic and human-in-the-loop methods. The work will enable the following applications: Safer and more efficient roads, monitoring the roadway infrastructure and roadside, maximizing the distribution services and informing decisions on health policy (including COVID-19). The project will be conducted in collaboration with several stakeholders - multiple infrastructure and traffic management startups and local city government - in a comprehensive transition to practice program designed to deploy the research in the real world.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.