Design of Resilient Smart Highway Systems with Data-Driven Monitoring from Networked Cameras

• How to accurately and robustly retrieve relevant information from not only an individual camera but networked cameras
• How to design a resilient control system that maximizes the use of camera data while minimizing the negative impact of sensor failures and error in data processing

These questions have to be jointly addressed, since the performance of automatic video monitoring, the deployment (number and locations) of surveillance cameras, and traffic control algorithm based on real-time information are closely related and interdependent.

This project aims to develop a systematic way to design smart highway systems with networked video monitoring and control resiliency against environment disruptions and sensor failures. The research team will investigate deep learning methods for extracting fine-grained local categorical traffic information from surveillance videos and novel graph neural network methods to correlate and propagate the local information through the highway network for global states estimation, such as vehicle tracking and reidentification or traffic prediction in an unobserved area. The expected outcome is an implementable approach to designing resilient smart highway systems with trustworthy monitoring capabilities.