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Machine Learning for Autonomous Driving
Rowan McAllister · Xinshuo Weng · Daniel Omeiza · Nick Rhinehart · Fisher Yu · German Ros · Vladlen Koltun

Fri Dec 11 07:55 AM -- 05:00 PM (PST) @
Event URL: https://ml4ad.github.io/ »

Welcome to the NeurIPS 2020 Workshop on Machine Learning for Autonomous Driving!

Autonomous vehicles (AVs) offer a rich source of high-impact research problems for the machine learning (ML) community; including perception, state estimation, probabilistic modeling, time series forecasting, gesture recognition, robustness guarantees, real-time constraints, user-machine communication, multi-agent planning, and intelligent infrastructure. Further, the interaction between ML subfields towards a common goal of autonomous driving can catalyze interesting inter-field discussions that spark new avenues of research, which this workshop aims to promote. As an application of ML, autonomous driving has the potential to greatly improve society by reducing road accidents, giving independence to those unable to drive, and even inspiring younger generations with tangible examples of ML-based technology clearly visible on local streets.

All are welcome to submit and/or attend! This will be the 5th NeurIPS workshop in this series. Previous workshops in 2016, 2017, 2018 and 2019 enjoyed wide participation from both academia and industry.

Author Information

Rowan McAllister (UC Berkeley)
Xinshuo Weng (Carnegie Mellon University)
Daniel Omeiza (University of Oxford)
Nick Rhinehart (UC Berkeley)
Fisher Yu (ETH Zurich)
German Ros (Intel Labs)
Vladlen Koltun (Intel Labs)

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