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Research Projects

Automated Discovery of Data Validity for Safety-Critical Feedback Control in a Population of Connected Vehicles

Databases can be forgetful - and that is a good thing!

Sponsor: National Science Foundation via Cyber Physical Systems (CPS) program

Senior investigators: Kshitij Jerath, Cindy Chen

Junior investigators: Hossein Haeri, Lorina Sinanaj, Niket Kathiriya, Rinith Pakala, Eric Fan, Usha Sravani Ganta

When does data expire? From ignoring Yelp food reviews from long ago, or bypassing Waze driving instructions that lead into construction zones, our society’s cyber-mediated actions depend on a trust in the validity of data stored, aggregated, and shared by remote databases that are updated in feedback with our decisions. The proposed work is motivated by a cyber-physical transportation application: fleets of connected and autonomous vehicles (CAVs) driving on potentially icy roads, where safety-critical road friction information is shared via a wireless data link to a central database that mediates data averaging. If there is no more snow, does your connected vehicle need to drive slow? Our implementation-focused approach has developed novel algorithms that can enable systematic forgetting of previously collected data to ensure that our cyber systems base their decisions only on data that is valid in the current context. We have demonstrated that significant quantities of data stored in repositories can be successfully forgotten and abstracted without losing validity for decision making. We have implemented this using both real-time database implementations and stream machine learning techniques. Our work has made novel inroads by proposing concepts such as adaptive granulation of data in databases, stream learning that prioritizes model stability, and near-optimal algorithmic forgetting of expired data.
Rescaled models retain traffic behavior.png

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