Machine Learning for Autonomous Driving: Recent Advances and Future Challenges
Tremendous progresses have been made in applying machine learning to autonomous driving. However, there are fundamental challenges ahead. In this talk, I will present recent advances in applying machine learning to solving the perception, prediction, planning and control problems of autonomous driving. I will discuss key research challenges in learning more robust and abstract representations, scene understanding, behavior prediction, and decision-making in complex real-world scenarios.
Li Erran Li is the head of machine learning at Scale AI and an adjunct professor at Columbia University. Previously, he was chief scientist at Pony.ai. Before that, he was with the perception team at Uber ATG and machine learning platform team at Uber where he worked on deep learning for autonomous driving, led the machine learning platform team technically, and drove strategy for company-wide artificial intelligence initiatives. He started his career at Bell Labs. Li’s current research interests are machine learning, computer vision, learning-based robotics, and their application to autonomous driving. He has a PhD from the computer science department at Cornell University. He is an ACM Fellow and IEEE Fellow.