CRII: CNS: Supporting Resilient Perception in Autonomous Cyber-physical Systems

Autonomous cyber-physical systems (A-CPS), including self-driving vehicles, environmental monitoring drones, and search-and-rescue robots, are expected to navigate challenging physical landscapes without human assistance. To ensure mission success, it is important to understand what internal or external conditions may cause A-CPS devices to fail. The state-of-the-art is to verify that A-CPS can operate in adverse conditions before deployment, followed by continuous monitoring post-deployment. However, it is difficult to anticipate all of the difficulties an A-CPS device may encounter once deployed in the real world, and if failure conditions are observed while the device is operating, it is typically too late to react. This project focuses on predicting failures for A-CPS ahead of time and injecting actions to avoid the failures. In particular, the project analyzes a state-of-the-art object detection application executing on a commercial-of-the-shelf embedded system, generates data-driven predictive models of failure using machine learning techniques, and implements a software manager to configure the system to avoid predicted failures. The success of the project will provide new insights into the relationship between physical environments and A-CPS behaviors and will enable developers to create more reliable and resilient algorithms with higher confidence. Autonomous cyber-physical systems that can navigate the physical world without human intervention could significantly improve quality of life and safety. For instance, California alone has over 31 million acres of wildlands, with high or very high fire hazard severity zones, spanning from the northern to the southern borders. Autonomous wildfire detection drones could help save lives of residents that are potential wildfire victims, as well as public servants responsible for monitoring high risk regions. To be successful, such systems must be able to handle difficult and unpredictable environmental conditions. 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.