Autonomous vehicles (AVs) are promising to increase transportation safety and security, but the state-of-the-art artificial intelligence (AI) technologies used in AVs are still not sufficient, as evident in fatal crashes involving AVs. In the foreseeable future, human inputs and interventions will still be necessary, at least as a monitor or supervisor in AVs. Monitoring to correctly detect rare but potentially deadly events in AVs requires high levels of vigilance. The required vigilance taxes human supervisors in AVs. This project aims to overcome these challenges through novel collaboration between the AI system and the human driver. This project will result in algorithms and design principles that help reduce road accidents and are broadly applicable to other related intelligent systems in critical areas such as cybersecurity, national defense, and healthcare. Moreover, this project will support multi-disciplinary training of graduate and undergraduate students across disciplines, the development of course modules that provide students interdisciplinary experience critical to shaping the regional and national workforce, and involvement of underrepresented students in STEM fields at the graduate, undergraduate, and pre-K through 12 levels.
This project addresses safety-critical challenges by developing a cognizant human-in-the-loop secure AI mechanism. The project focuses on autonomous driving incorporating three thrusts: (1) Investigate how to maintain human drivers' vigilance through secondary task assignments that incorporate the level of uncertainty in the AI decisions, (2) Develop a fault-tolerant, adversary-aware AI engine that outputs uncertainty levels in its decision as a basis for requesting human inputs. (3) Develop a vigilance-based adaptive task-allocation scheme to calibrate human vigilance online based on a quantitative vigilance model constructed from human-subject data.
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.