Abstract. AI models are ubiquitous in modern autonomy stacks, enabling tasks such as perception and prediction. However, providing safety assurances for such models represents a major challenge, due in part to their data-driven design and dynamic behavior. I will present recent results on building trust in AI models for autonomous vehicles, along three main directions: (1) data-driven traffic models for closed-loop simulation and safety assessment of autonomy stacks; (2) techniques to provide calibrated uncertainty estimates for AI models leveraging ideas from conformal prediction theory; and (3) tools to monitor AI components at run-time, with an emphasis on detecting semantic anomalies through the use of large language models. The discussion will be grounded in autonomous driving and aerospace robotics applications.

Bio. Dr. Marco Pavone is an Assistant Professor of Aeronautics and Astronautics at Stanford University, where he is the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He is a recipient of several awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an ONR Young Investigator Award, an NSF CAREER Award, and a NASA Early Career Faculty Award. He was identified by the American Society for Engineering Education (ASEE) as one of America's 20 most highly promising investigators under the age of 40. His work has been recognized with best paper nominations or awards at the International Conference on Intelligent Transportation Systems, at the Field and Service Robotics Conference, at the Robotics: Science and Systems Conference, and at NASA symposia.