(video) Building Trust in AI for Autonomous Vehicles | Nuro Technical Talks

About the Talk: 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. There will be presented recent results on building trust in AI models for autonomous vehicle systems, 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.

About the Speaker: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he directs the Autonomous Systems Laboratory and the Center for Automotive Research at Stanford. He also serves as Director of Autonomous Vehicle Research at NVIDIA. 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 a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. 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.


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