Workshop
Mon Dec 13 07:50 AM -- 06:30 PM (PST)
Machine Learning for Autonomous Driving
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) UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning (Oral) | |
| Poster Session and Social | |
| Break | |
| Physics-Guided AI for Modeling Autonomous Vehicle Dynamics (Keynote Talk) | |
| Q&A: Rose Yu (Live Q/A) | |
| The Ongoing Research in University of Michigan & Ford Center for Autonomous Vehicles (FCAV) (Keynote Talk) | |
| Q&A: Matthew Johnson-Roberson (Live Q/A) | |
| CARLA Challenge (Challenge) | |
| Break | |
| Fantastic Failures and Where to Find Them: Designing Safe, Robust Autonomy (Keynote Talk) | |
| Q&A: Katie Driggs-Campbell (Live Q/A) | |
| Safely Learning Behaviors of Other Agents (Keynote Talk) | |
| Q&A: Claire Tomlin (Live Q/A) | |
| Spotlight Talks (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 | |
| Real-time Generalized Sensor Fusion with Transformers (Poster) | |
| Fast Polar Attentive 3D Object Detection based on Point Cloud (Poster) | |
| UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning (Poster) | |
| Switching Recurrent Kalman Networks (Poster) | |
| Scalable Primitives for Generalized Sensor Fusion in Autonomous Vehicles (Poster) | |
| Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models (Poster) | |
| Are Socially-Aware Trajectory Prediction Models Really Socially-Aware? (Poster) | |
| Efficient Unknown Object Detection with Discrepancy Networks for Semantic Segmentation (Poster) | |
| Self-supervised Sun Glare Detection CNN for Self-aware Autonomous Driving (Poster) | |
| Watch out for the risky actors: Assessing risk in dynamic environments for safe driving (Poster) | |
| A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation (Poster) | |
| Temporal Transductive Inference for Few-Shot Video Object Segmentation (Poster) | |
| Spatial-Temporal Gated Transformersfor Efficient Video Processing (Poster) | |
| How Far Can I Go ? : A Self-Supervised Approach for Deterministic Video Depth Forecasting (Poster) | |
| TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference (Poster) | |
| DriverGym: Democratising Reinforcement Learning for Autonomous Driving (Poster) | |
| Object-Level Targeted Selection via Deep Template Matching (Poster) | |
| Self-Supervised Pretraining for Scene Change Detection (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) | |
| Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction (Poster) | |
| NSS-VAEs: Generative Scene Decomposition for Visual Navigable Space Construction (Poster) | |
| PKCAM: Previous Knowledge Channel Attention Module (Poster) | |
| Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement (Poster) | |
| Improved Object Detection in Thermal Imaging Through Context Enhancement and Information Fusion: A Case Study in Autonomous Driving (Poster) | |
| Circular-Symmetric Correlation Layer (Poster) | |
| Monocular 3D Object Detection by Leveraging Self-Supervised Visual Pre-training (Poster) | |
| Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars (Poster) | |
| ORDER: Open World Object Detection on Road Scenes (Poster) | |
| Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection (Poster) | |
| AA3DNet: Attention Augmented Real Time 3D Object Detection (Poster) | |
| A Scenario-Based Platform for Testing Autonomous Vehicle Behavior Prediction Models in Simulation (Poster) | |
| PolyTrack: Tracking with Bounding Polygons (Poster) | |
| MTL-TransMODS: Cascaded Multi-Task Learning for Moving Object Detection and Segmentation with Unified Transformers (Poster) | |