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Mon Dec 13 07:50 AM -- 06:30 PM (PST)
Machine Learning for Autonomous Driving
Xinshuo Weng · Jiachen Li · Nick Rhinehart · Daniel Omeiza · Ali Baheri · Rowan McAllister

Workshop Home Page

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.

Opening Remarks
Reinforcement Learning for Autonomous Driving (Keynote Talk)
Q&A: Jeff Schneider (Live Q/A)
AV2.0: Deploying End to End Deep Learning Policies at Fleet Scale (Keynote Talk)
Q&A: Alex Kendall (Live Q/A)
Best Paper (Oral)
Poster Session and Social
Physics-Guided Learning for Modeling Autonomous Vehicle Dynamics (Keynote Talk)
Q&A: Rose Yu (Live Q/A)
Prediction, Pedestrians, and Planning Guarantees and Safety for AVs (Keynote Talk)
Q&A: Matthew Johnson-Roberson (Live Q/A)
CARLA Challenge (Challenge)
Fantastic Failures and Where to Find Them: Designing Safe, Robust Autonomy (Keynote Talk)
Q&A: Katie Driggs-Campbell (Live Q/A)
Keynote Talk (Claire Tomlin), Title TBD (Keynote Talk)
Q&A: Claire Tomlin (Live Q/A)
Best Student Paper (Oral)
Poster Session and Social
Learning Driving Agents from Simulation (Keynote Talk)
Q&A: Mark Palatucci (Live Q/A)
Autonomous Vehicle Decision-Making Policy Fast Adaptation Using Meta Reinforcement Learning (Keynote Talk)
Q&A: Songan Zhang (Live Q/A)
Robotics for an ML-Driven World (Keynote Talk)
Q&A: Sarah Tang (Live Q/A)
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks (Challenge)
Closing Remarks
How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting (Poster)
A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation (Poster)
Watch out for the risky actors: Assessing risk in dynamic environments for safe driving (Poster)
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation (Poster)
Object-Level Targeted Selection via Deep Template Matching (Poster)
Self-Supervised Pretraining for Scene Change Detection (Poster)
DriverGym: Democratising Reinforcement Learning for Autonomous Driving (Poster)
Self-supervised Sun Glare Detection CNN for Self-aware Autonomous Driving (Poster)
Real-time Generalized Sensor Fusion with Transformers (Poster)
PKCAM: Previous Knowledge Channel Attention Module (Poster)
Efficient Unknown Object Detection with Discrepancy Networks for Semantic Segmentation (Poster)
MTL-TransMODS: Cascaded Multi-Task Learning for Moving Object Detection and Segmentation with Unified Transformers (Poster)
Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement (Poster)
TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference (Poster)
UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning (Poster)
Fast Polar Attentive 3D Object Detection based on Point Cloud (Poster)
NSS-VAEs: Generative Scene Decomposition for Visual Navigable Space Construction (Poster)
Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection (Poster)
Circular-Symmetric Correlation Layer (Poster)
Temporal Transductive Inference for Few-Shot Video Object Segmentation (Poster)
Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction (Poster)
Does Thermal data make the detection systems more reliable? (Poster)
Reinforcement Learning as an Alternative to Reachability Analysis for Falsification of AD Functions (Poster)
Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models (Poster)
Monocular 3D Object Detection by Leveraging Self-Supervised Visual Pre-training (Poster)
Spatial-Temporal Gated Transformersfor Efficient Video Processing (Poster)
Scalable Primitives for Generalized Sensor Fusion in Autonomous Vehicles (Poster)
AA3DNet: Attention Augmented Real Time 3D Object Detection (Poster)
Improved Object Detection in Thermal Imaging Through Context Enhancement and Information Fusion: A Case Study in Autonomous Driving (Poster)
ORDER: Open World Object Detection on Road Scenes (Poster)
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars (Poster)
Switching Recurrent Kalman Networks (Poster)
PolyTrack: Tracking with Bounding Polygons (Poster)