Establishing Correctness of Learning-Enabled Autonomous Systems

Tichakorn (Nok) Wongpiromsarn is an Assistant Professor in the Department of Computer Science at Iowa State University, where she also serves as the director of the Autonomous Systems Laboratory. In addition to her academic role, she holds a Visiting Academic position with Amazon Robotics. She received her Ph.D. in Mechanical Engineering from the California Institute of Technology in 2010. Her research interests lie in the broad area of computer science, control theory, and optimization, with a particular focus on the design and analysis of autonomous systems. A significant portion of her career has been devoted to the development of autonomous vehicles,
both in academic and industry settings. Before joining ISU, she held the position of principal research scientist and led the planning team at nuTonomy (now Motional). She is a recipient of an NSF CAREER Award in 2022.

Keynote Abstract

Autonomous systems are subject to multiple regulatory requirements due to their safety-critical nature. In general, it may not be feasible to guarantee the satisfaction of all requirements under all conditions. In such situations, the system needs to decide how to prioritize among them. Two main factors complicate this
decision. First, the priorities among the conflicting requirements may not be fully established. Second, the decision needs to be made under uncertainties arising from both the learning-based components within the system and the unstructured, unpredictable, and non-cooperating nature of the environments. Therefore, establishing the correctness of autonomous systems requires specification languages that capture the unequal importance of the requirements, quantify the violation of each requirement, and incorporate uncertainties faced by the systems. In this talk, I will discuss our early effort to partially address this problem and the remaining challenges.