NeurIPS 2021
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Machine Learning for Autonomous Driving

Xinshuo Weng · Jiachen Li · Nick Rhinehart · Daniel Omeiza · Ali Baheri · Rowan McAllister

We propose a full-day workshop, called “Machine Learning for Autonomous Driving” (ML4AD), as a venue for machine learning (ML) researchers to discuss research problems concerning autonomous driving (AD). Our goal is to promote ML research, and its real-world impact, on self-driving technologies. Full self-driving capability (“Level 5”) is far from solved and extremely complex, beyond the capability of any one institution or company, necessitating larger-scale communication and collaboration, which we believe workshop formats help provide.

We propose a large-attendance talk format of approximately 500 attendees, including (1) a call for papers with poster sessions and spotlight presentations; (2) keynote talks to communicate the state-of-the-art; (3) panel debates to discuss future research directions; (4) a call for challenge to encourage interaction around a common benchmark task; (5) social breaks for newer researchers to network and meet others.

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