ECCS-EPSRC - ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems

Rowan University, Glassboro NJ

Investigators

Abstract

Modern sensors generate massive amounts of data. Algorithms that are data-driven, able to train themselves or "self-tune", have revolutionized the area of autonomous systems. However, capturing confidence from the integration of heterogeneous large-scale data remains a challenging task for these algorithms. Our work will develop pioneering approaches that will introduce safe and reliable autonomy at different levels in sensor-driven systems. The main focus is on machine learning methods with quantified uncertainty or confidence bounds for the provided solutions. This research will entail significant theoretical knowledge through formal development, analysis and evaluation of the proposed approaches, resulting in safe and reliable machine intelligence. Scalable, effective and robust algorithms will be available for one of the most critical challenges in computational intelligence. Understanding and assessing the uncertainty of modern machine learning models has critical consequences, especially when the output of such models is fed into higher-level decision making processes. These include autonomous drones and vehicles, diagnosis in the medical domain and surveillance. Case studies and applications of this research include industry and government partners in the areas of detection of weapon and contraband (in collaboration with the Transportation Security Laboratory at the US Department of Homeland Security), rotorcraft safety (US Federal Aviation Administration), intelligent transportation systems (UK Highways England, Valerann Ltd UK, and TransDAC, US), and smart cities and surveillance (QinetiQ Ltd UK, Thales Ltd). Open-source software libraries, based on state-of-the-art deep learning frameworks, e.g., TensorFlow and PyTorch, will be built for the rapid dissemination of the developed core computing techniques. The proposed effort also includes integrating the research into the undergraduate and graduate curriculums, developing outreach packages which deliver hands-on experiences covering the fields of autonomous monitoring and the issues facing future intelligent transportation systems and the cities as a whole. 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.