Connected Automated Vehicles (CAVs), often referred to as “self-driving cars,” will have a profound impact not only on transportation systems, but also in terms of associated economic, environmental, and social effects. As with any such major transformative undertaking, quantifying the magnitude of its expected impact is essential.

We describe a Cyber-Physical system approach to Photovoltaic (PV) array control. A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel. Our approach promises to improve efficiency in renewable energy systems using cyber-enabled sensory analysis and fusion.

A new method for generalizing LMIs to infinite-dimensional systems is developed. This allows optimal estimator and controller synthesis for systems with multiple delays and multiple states with no approximation and no detectable conservatism. Currently, the supporting software can handle systems when the number of states times the number of delays is less than 50.

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications.