Foundation Models for Autonomous Driving

Part of the Special ECE Seminar Series

Modern Artificial Intelligence

Title:

Foundation Models for Autonomous Driving

Speaker:

Dr. Marco Pavone, Stanford University

 Abstract:

Foundation models, trained on vast and diverse data encompassing the human experience, are at the heart of the ongoing AI revolution influencing the way we create, problem solve, and work. These models, and the lessons learned from their construction, can also be applied to the way we develop a similarly transformative technology, autonomous vehicles. In this talk, I will highlight recent research efforts towards rethinking elements of an AV program both in the vehicle and in the data center, with an emphasis on (1) composing ingredients for universal and controllable end-to-end simulation, (2) architecting autonomy stacks that leverage foundation models to generalize to long-tail events, and (3) ensuring safety with foundation models in the loop.

Bio:

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 leads 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.