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Machine Learning for Autonomous Driving
Jiachen Li · Nigamaa Nayakanti · Xinshuo Weng · Daniel Omeiza · Ali Baheri · German Ros · Rowan McAllister

Sat Dec 03 06:20 AM -- 03:00 PM (PST) @ Theater B
Event URL: https://ml4ad.github.io/ »

Welcome to the NeurIPS 2022 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 attend! This will be the 7th NeurIPS workshop in this series. Previous workshops in 2016, 2017, 2018, 2019, 2020, and 2021 enjoyed wide participation from both academia and industry.

Author Information

Jiachen Li (Stanford University)
Nigamaa Nayakanti (Google)
Xinshuo Weng (Carnegie Mellon University)
Daniel Omeiza (Department of Computer Science, University of Oxford)
Ali Baheri (West Virginia University)
German Ros (Intel)
Rowan McAllister (Toyota Research Institute)

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