Safe Learning for Autonomous Driving

ICML 2022 Workshop + Challenge


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 .

Time
Event
Content
08:50
Welcome
Opening Remarks
09:00
Ivan Istomin

Ivan Istomin

Roborace

Talk title TBD
15:30
Peter Stone

Peter Stone

UT Austin; Sony AI

Talk title TBD
10:00
Poster Session + Gathertown
11:00
Spotlight Talks
11:30
Melanie Zeilinger

Melanie Zeilinger

ETH Zürich

Talk title TBD
12:00
Lunch + Social
13:30
Autonomous Racing Virtual Challenge: Contributed Talks
14:00
Spotlight Talks
11:30
Todd Hester

Todd Hester

Amazon Scout

Talk title TBD
09:30
Chelsea Finn

Chelsea Finn

Stanford + Google Brain

Talk title TBD
16:00
Break, Social, and Posters
09:30
Andrea Bajcsy

Andrea Bajcsy

UC Berkeley

Safety Assurances for Human-Robot Interaction via Confidence-aware Game-theoretic Human Models
17:30
Kumar Chellapilla

Kumar Chellapilla

Amazon AWS

Talk title TBD
18:00
David Held

David Held

CMU

Talk title TBD
18:00
Sergey Levine

Sergey Levine

UC Berkeley

Reinforcement Learning as Data-Driven Decision Making
18:30
Conclusion
Closing Remarks

Speakers

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!

L2R Autonomous Racing Virtual Challenge: Safe Learning for Autonomous Driving
L2R Autonomous Racing Virtual Challenge: Safe Learning for Autonomous Driving
L2R Autonomous Racing Virtual Challenge: Safe Learning for Autonomous Driving
L2R Autonomous Racing Virtual Challenge: Safe Learning for Autonomous Driving

Steps to victory!

Organisers

Jonathan Francis

Jonathan Francis

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

Hitesh Arora

Hitesh Arora

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

Bingqing Chen

Bingqing Chen

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

Xinshuo Weng

Xinshuo Weng

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

Siddha Ganju

Siddha Ganju

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

Daniel Omeiza

Daniel Omeiza

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

Jean Oh

Jean Oh

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

Eric Nyberg

Eric Nyberg

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

Sylvia Herbert

Sylvia Herbert

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

Li Erran Li

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

Sponsors