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Machine Learning for Autonomous Driving
Rowan McAllister · Nicholas Rhinehart · Fisher Yu · Li Erran Li · Anca Dragan

Sat Dec 14 08:00 AM -- 06:00 PM (PST) @ East Meeting Rooms 1 - 3
Event URL: https://ml4ad.github.io/ »

Autonomous vehicles (AVs) provide a rich source of high-impact research problems for the machine learning (ML) community at NeurIPS in diverse fields including computer vision, probabilistic modeling, gesture recognition, pedestrian and vehicle forecasting, human-machine interaction, and multi-agent planning. The common goal of autonomous driving can catalyze discussion between these subfields, generating a cross-pollination of research ideas. Beyond the benefits to the research community, AV research can improve society by reducing road accidents; giving independence to those unable to drive; and inspiring younger generations towards ML with tangible examples of ML-based technology clearly visible on local streets.

As many NeurIPS attendees are key drivers behind AV-applied ML, the proposed NeurIPS 2019 Workshop on Autonomous Driving intends to bring researchers together from both academia and industries to discuss machine learning applications in autonomous driving. Our proposal includes regular paper presentations, invited speakers, and technical benchmark challenges to present the current state of the art, as well as the limitations and future directions for autonomous driving.

Author Information

Rowan McAllister (UC Berkeley)
Nicholas Rhinehart (Carnegie Mellon University)

Nick Rhinehart is a Postdoctoral Scholar in the Electrical Engineering and Computer Science Department at the University of California, Berkeley, where he works with Sergey Levine. His work focuses on fundamental and applied research in machine learning and computer vision for behavioral forecasting and control in complex environments, with an emphasis on imitation learning, reinforcement learning, and deep learning methods. Applications of his work include autonomous vehicles and first-person video. He received a Ph.D. in Robotics from Carnegie Mellon University with Kris Kitani, and B.S. and B.A. degrees in Engineering and Computer Science from Swarthmore College. Nick's work has been honored with a Best Paper Award at the ICML 2019 Workshop on AI for Autonomous Driving and a Best Paper Honorable Mention Award at ICCV 2017. His work has been published at a variety of top-tier venues in machine learning, computer vision, and robotics, including AAMAS, CoRL, CVPR, ECCV, ICCV, ICLR, ICML, ICRA, NeurIPS, and PAMI. Nick co-organized the workshop on Machine Learning in Autonomous Driving at NeurIPS 2019, the workshop on Imitation, Intent, and Interaction at ICML 2019, and the Tutorial on Inverse RL for Computer Vision at CVPR 2018.

Fisher Yu (Princeton University)
Li Erran Li (Scale AI)

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 ACM Fellow and IEEE Fellow.

Anca Dragan (UC Berkeley)

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