Until now, the cyber component of automobiles has consisted of control algorithms and associated software for vehicular subsystems designed to achieve one or more performance, efficiency, reliability, comfort, or safety goals, primarily based on short-term intrinsic vehicle sensor data. However, there exist many extrinsic factors that can affect the degree to which these goals can be achieved. These factors can be determined from: longer-term traces of in-built sensor data that can be abstracted as triplines, socialized versions of these that are shared amongst vehicle users, and online databases. These three sources of information collectively constitute the automotive infoverse. This project harnesses this automotive infoverse to achieve these goals through high-confidence vehicle tuning and driver feedback decisions. Specifically, the project develops software called Headlight that permits the rapid development of apps that use the infoverse to achieve one or more goals. Advisory apps can provide feedback to the driver in order to ensure better fuel efficiency, while auto-tuning goals can set car parameters to promote safety. Allowing vehicles and such apps to share vehicle data with others and to use extrinsic information results in novel information processing, assurance, and privacy challenges. The project develops methods, algorithms and models to address these challenges. Broader Impact - This project can have significant societal impact by reducing carbon emissions and improving vehicular safety, can spur innovation in tuning methods and encourage researchers to experiment with this class of cyber-physical systems. The active participation of General Motors will strongly facilitate technology transfer. The program has outreach through internships, course material, high school and undergraduate involvement, and through creating an open infrastructure usable by diverse developers.
Performance Period: 10/01/2013 - 09/30/2019
Sponsor: National Science Foundation
Award Number: 1330118