Overcoming the Most Difficult Challenges in Autonomous Vehicles
Drago joined Waymo in 2018 to lead the Research team, which focuses on pushing the state of the art in autonomous driving using machine learning. Earlier in his career he spent eight years at Google; first working on 3D vision and pose estimation for StreetView, and later leading a research team which developed computer vision systems for annotating Google Photos. The team also invented popular methods such as the Inception neural network architecture, and the SSD detector, which helped win the Imagenet 2014 Classification and Detection challenges. Prior to joining Waymo, Drago led the 3D Perception team at Zoox.
Drago joined Waymo in 2018 to lead the Research team, which focuses on pushing the state of the art in autonomous driving using machine learning. Earlier in his career he spent eight years at Google; first working on 3D vision and pose estimation for StreetView, and later leading a research team which developed computer vision systems for annotating Google Photos. The team also invented popular methods such as the Inception neural network architecture, and the SSD detector, which helped win the Imagenet 2014 Classification and Detection challenges. Prior to joining Waymo, Drago led the 3D Perception team at Zoox.
Dr. Marco Pavone is an Associate 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. He is currently on a partial leave of absence at NVIDIA serving as Director of Autonomous Vehicle Research. 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. 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.
Dr. Marco Pavone is an Associate 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. He is currently on a partial leave of absence at NVIDIA serving as Director of Autonomous Vehicle Research. 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. 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.
Alex Kendall, co-founder and CEO Wayve, a London start-up pioneering a next-generation autonomous driving system based on machine learning. Widely recognized as a world expert in this field, Alex was a research fellow at Cambridge University where he earned his Ph.D. in Computer Vision and Robotics. His research has received numerous awards for scientific impact, and he has been named on the Forbes 30 Under 30 innovators list. Alex's leadership and vision have led Wayve to become one of the most exciting start-ups in the burgeoning autonomous vehicle market.
Alex Kendall, co-founder and CEO Wayve, a London start-up pioneering a next-generation autonomous driving system based on machine learning. Widely recognized as a world expert in this field, Alex was a research fellow at Cambridge University where he earned his Ph.D. in Computer Vision and Robotics. His research has received numerous awards for scientific impact, and he has been named on the Forbes 30 Under 30 innovators list. Alex's leadership and vision have led Wayve to become one of the most exciting start-ups in the burgeoning autonomous vehicle market.
Kate Park works on Tesla Autopilot as the first and now lead AI PM for its computer vision team. At Tesla, Kate runs the "data engine," the process by which we improve neural networks via data. Kate received her bachelor's degree with distinction at Stanford in computer science in the artificial intelligence track (Phi Beta Kappa, Tau Beta Pi) and is the recipient of Stanford F. E. Terman Award. She has published award-winning research on spoken natural language processing, U.S. patents co-authored with Elon Musk, and a travel memoir. Kate is interested in machine learning platforms and autonomous systems.
Kate Park works on Tesla Autopilot as the first and now lead AI PM for its computer vision team. At Tesla, Kate runs the "data engine," the process by which we improve neural networks via data. Kate received her bachelor's degree with distinction at Stanford in computer science in the artificial intelligence track (Phi Beta Kappa, Tau Beta Pi) and is the recipient of Stanford F. E. Terman Award. She has published award-winning research on spoken natural language processing, U.S. patents co-authored with Elon Musk, and a travel memoir. Kate is interested in machine learning platforms and autonomous systems.
Experts working on autonomous vehicles will discuss the rapid pace of research and innovation in machine learning, highlighting the most exciting developments over the last few years and how they approach incorporating the constant advances into an autonomous vehicle safely. The panelists are industry leaders working at NVIDIA, Tesla, Waymo, and Wayve. They will also discuss the unique approaches each OEM takes to leverage machine learning in its self-driving stack, with some using end-to-end learning and others preferring modular learning, and each method's advantages and disadvantages. They will also discuss best practices for integrating complicated sensor suites, software, data management, and machine learning with engineering. Other challenges they will cover include collecting large amounts of data, managing, and labeling datasets, integrating ML models with the rest of the self-driving stack, and how to improve the driver continuously. They will also discuss the critical role of simulation in development, and the current state of self-driving cars and the most difficult challenges they face today, including scaling to new environments quickly.