Machine learning for autonomous driving: Recent advances and future challenges
Who is this presentation for?
- Researchers and engineers working on machine learning for autonomous driving
Tremendous progress has been made in applying machine learning to autonomous driving. However, there are still fundamental challenges ahead.
Li Erran Li explores recent advances in applying machine learning to solving the perception, prediction, planning, and control problems of autonomous driving as well as some key research challenges in learning more robust and abstract representations, scene understanding, behavior prediction, and decision making in complex real-world scenarios.
- Familiarity with deep learning
What you'll learn
- Learn the recent advances and key research challenges in applying machine learning to solving the problems of autonomous driving
Li Erran Li
Scale | Columbia University
Li Erran Li is the head of machine learning at Scale 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’s an IEEE Fellow and an ACM Fellow.
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