CFP: NeurIPS 2020 Workshop on ML for Autonomous Driving (ML4AD 2020)

[Rowan]

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Call for Papers to Machine Learning for Autonomous Driving workshop at NeurIPS 2020

Submit by: 14th October 2020 AoE

Workshop: 11th or 12th December 2020

Details: https://ml4ad.github.io/

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ABOUT

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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.

SPEAKERS

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Angela Schoellig, University of Toronto

Dragomir Anguelov, Waymo

Beipeng Mu, Momenta.ai

Ehud Sharlin, University of Calgary

Pin Wang, UC Berkeley

Jianxiong Xiao, AutoX

Sertac Karaman, MIT & Optimus Ride

Patrick Perez, Valeo

Byron Boots, University of Washington

SUBMISSION GUIDELINES

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Interested researchers from both, academia or industry are invited to submit either extended abstracts (4 pages) or full papers (8 pages) anonymously. References and appendix do not count towards the page count.

Accepted papers will be invited to present a talk at the workshop.

Submission Website: https://cmt3.research.microsoft.com/ML4AD2020

LaTeX Template: https://ml4ad.github.io/#submit

ORGANIZERS

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Rowan McAllister, UC Berkeley

Xinshuo Weng, CMU

Daniel Omeiza, Oxford

Nicholas Rhinehart, UC Berkeley

Fisher Yu, ETH

German Ros, Intel

Vladlen Koltun, Intel