Our world is currently experiencing an incredible increase in the amount of real-world data available, yet that data remains useful or valid only for a finite period of time. For example, detour information provided to drivers during traffic construction loses its utility upon completion of the construction assignment. This project develops methods to determine the validity of data accumulated in databases, to answer the question: when do data expire? Knowledge of data validity is even more important in the context of safety-critical applications in the physical world: how much of the past data should be trusted to make safety-critical decisions in the present? Can data from nearby locations be trusted to accurately reflect local context and conditions? Answering these fundamental questions will impact a wide-range of applications, including traffic management, national defense, weather forecasting, etc., since data is a universal feature of modern society. The methods developed in this project are implemented and tested for control of connected autonomous vehicles in safety-critical scenarios such as driving on potentially icy roads. This work has significant potential to not only ensure safety in the imminent deployment of connected autonomous vehicles, but also improve certainty and confidence in a wide range of data- and information- intensive applications. This collaborative research will support development of graduate and undergraduate researchers at Penn State University, Bucknell University, and the University of Massachusetts Lowell. The project also includes Science, Technology, Engineering, and Math (STEM)-focused outreach activities for middle-school students to broaden participation within the field of cyber-physical systems.
The research objective of the project is to create methods to determine how the validity of data decays over time, and over increasing distances away from where the data was collected. The research is conducted in the context of safety-critical systems, namely fleets of connected autonomous vehicles (CAVs) driving on potentially icy roads, where safety-critical road friction information is shared via a wireless data link to a central spatiotemporal database that mediates data averaging. This data is used to estimate the roadway friction coefficient (i.e. the presence of ice) and is transmitted to other connected vehicles in the vicinity. The time duration of data trust and validity of the friction estimates within the database are evaluated using Allan variance analysis, enabling the database to internally model and monitor data timeliness and quality. The investigators also study performance metrics (e.g., stability) of the coupled fast and slow feedback loops, where the fast loop acts at the vehicle level to ensure safe CAV operations in icy conditions using database-mediated preview of friction measurements. The slow loop is the spatial, multi-vehicle data averaging in the database using current measurements provided by a fleet of CAVs. These functionalities are then examined in the context of CAV fleets operating on road networks with large spatiotemporal extents. While implemented in a CAV context, these methods can be used in any application that synthesizes actionable information from spatial, temporal, or spatiotemporal data streams.
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.
