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Sat Dec 14 08:00 AM -- 06:00 PM (PST) @ East Meeting Rooms 1 - 3
Machine Learning for Autonomous Driving
Rowan McAllister · Nicholas Rhinehart · Fisher Yu · Li Erran Li · Anca Dragan

Workshop Home Page

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.

Invited Talk
Coffee + Posters (Break)
Towards Robust Interactive Autonomy (Invited Talk)
Human-inspired AI for autonomous driving (Invited Talk)
ArgoAI Challenge (Challenge Talk)
Lunch (Break)
Invited Talk
DiDi Challenge (Challenge Talk)
Invited Talk
Patch Refinement - Localized 3D Object Detection (Contributed Talk)
Conditional Flow Variational Autoencoders for Structured Sequence Prediction (Contributed Talk)
Coffee + Posters (Break)
Meta Learning Deep Visual Words for Fast Video Object Segmentation (Contributed Talk)
Urban Driving With Conditional Imitation Learning (Contributed Talk)
Safety and Interaction: the Game Theory of Autonomous Vehicles (Invited Talk)
Mixed Autonomy Traffic: A Reinforcement Learning Perspective (Invited Talk)