About
Welcome to the 1st Workshop on Safe Learning for Autonomous Driving (SL4AD), co-located with the International Conference on Machine Learning (ICML 2022), to be held on 22 July 2022 in hybrid format.
While there have been significant advances in vehicle autonomy (e.g., perception, trajectory forecasting, planning and control, etc.), it is of paramount importance for autonomous systems to adhere to safety specifications, as any safety infraction in urban and highway driving, or high-speed racing could lead to catastrophic failures. Given this inherent tension between safety and performance, we introduce a new simulation environment in autonomous racing as a particularly challenging proving ground for safe learning algorithms.
We envision this workshop bringing together researchers and industry practitioners from different AI subfields to work towards safer and more robust autonomous technology. We encourage participants to take part in the Challenge by competing for top leaderboard positions, to submit articles for review, and to engage with us at ICML 2022.
For more information on the tracks and submission topics, please review our Call for Papers page: https://learn-to-race.org/workshop-sl4ad-icml2022/calls.html
Dates
Note: all deadlines are in Eastern Daylight Time (EDT), UTC -4, New York.
Paper Submission
Submissions open: 23 March 2022
Submissions due: 20 May 2022
Reviewing starts: 21 May 2022
Reviewing ends: 4 June 2022
Notification: 6 June 2022
Camera Ready + Video uploaded to Slideslive: 17 June 2022
Poster image due: 10 July 2022
Workshop
Event: 22 July 2022
Schedule
Friday, 22 July, 2022. All times are in Eastern Daylight Time (EDT). Current time is .

Ivan Istomin
Roborace

Peter Stone
UT Austin; Sony AI

Melanie Zeilinger
ETH Zürich

Todd Hester
Amazon Scout

Chelsea Finn
Stanford + Google Brain

Andrea Bajcsy
UC Berkeley

Kumar Chellapilla
Amazon AWS

David Held
CMU

Sergey Levine
UC Berkeley
Speakers

Ivan Istomin
Chief Metaverse Officer
Roborace

Peter Stone
Professor; Exec. Director, Sony AI America; Assoc. Chair, CS
UT Austin

Melanie Zeilinger
Assistant Professor
ETH Zürich

Todd Hester
Applied Scientist Lead
Amazon Scout

Chelsea Finn
Assistant Professor, Stanford; Google Brain

Andrea Bajcsy
UC Berkeley

Kumar Chellapilla
General Manager, ML/AI Services
Amazon AWS

David Held
Assistant Professor
Carnegie Mellon University

Sergey Levine
Associate Professor
UC Berkeley
Challenge
We also feature an exciting and new AI Challenge in high-speed autonomous racing. Here, the goal is to evaluate the joint safety, performance, and generalisation capabilities of perception and control algorithms, as they operate simulated Formula-style racing vehicles at their physical limits! The Learn-to-Race Autonomous Racing Virtual Challenge is now active. Participate now!
Organisers

Jonathan Francis
PhD candidate at CMU, Research Scientist at Bosch; domain knowledge-enhanced representation learning, applied to robotics and autonomous driving

Hitesh Arora
Researcher at Amazon, focusing on multimodal perception and reinforcement learning, applied to autonomous driving

Bingqing Chen
PhD candidate at CMU, focusing on constraint-based optimisation, physical mechanisms, and safe learning, applied to autonomous driving

Xinshuo Weng
PhD candidate at CMU, Research Scientist at NVIDIA; focusing on 3D computer vision and generative models for autonomous systems

Siddha Ganju
Researcher and Data Scientist at NVIDIA, focusing on computer vision optimization for vehicle autonomy and medical instruments

Daniel Omeiza
PhD student at Oxford, focusing on explainable AI and decision-making, in autonomous driving

Jean Oh
Research Professor in Robotics Institute at CMU and Director of Bot Intelligence Group; multimodal perception, navigation, and artificial intelligence

Eric Nyberg
Professor of Computer Science at CMU and Program Director, Masters of Computational Data Science; hybrid reasoning systems and artificial intelligence

Sylvia Herbert
Assistant Professor at UCSD and Director of Safe Autonomous Systems Lab; uncertainty modeling in control, safety-aware learning, autonomy

Li Erran Li
Head of Science/HIL at Amazon AWS AI and Adjunct Professor at Columbia; Computer Vision, Machine Learning Systems, and Algorithms
Program Committee
- Eren Aksoy
- Dazhi Cheng
- Tiago Cortinhal
- Henggang Cui
- Xiaoxiao Du
- Hesham Eraqi
- Xiangyu Gao
- S. Alireza Golestaneh
- Zehao Huang
- Fabian Hüger
- Arec Jamgochian
- Jian Li
- Zhenning Li
- Kevin Luo
- Wenhao Luo
- Yiwei Lyu
- Yunze Man
- Kunal Menda
- Praveen Palanisamy
- Daniele Reda
- Nazmus Sakib
- Aman Sinha
- Ashutosh Singh
- Pranjay Shyam
- Mark Schutera
- Adam Scibior
- Zhaoen Su
- Arun Balajee Vasudevan
- Ram Vasudevan
- Moritz Werling
- Yujie Wei
- Weiran Yao