Artificial Intelligence for Autonomous Driving

IJCAI 2022 Workshop + Challenge


About

Welcome to the 2nd IJCAI Workshop on Artificial Intelligence for Autonomous Driving (AI4AD)!

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 IJCAI 2022.

For more information on the tracks and submission topics, please review our Call for Papers page: https://learn-to-race.org/workshop-ai4ad-ijcai2022/calls.html

Dates

Note: all deadlines are in Central European Time (CET), UTC +1, Paris, Brussels, Vienna.

Paper Submission

Submissions open: 3 March 2022
Submissions due:

13 May 2022

 20 May 2022


Notification: 3 June 2022
Camera Ready + Video upload: 17 June 2022

Workshop

Event: 23 July 2022

Schedule

Saturday, 23 July, 2022. All times are in Central European Time (CET). Current time is .

Time
Event
Content
08:50
Welcome
Opening Remarks
09:00
Max Kumskoy

Max Kumskoy

ARRIVAL

Talk title TBD
09:30
Sahika Genc

Sahika Genc

Amazon AWS

Talk title TBD
10:00
Poster Session + Gathertown
11:00
Spotlight Talks
11:30
Jaime Fisac

Jaime Fisac

Princeton

Game-Theoretic Autonomous Driving: the multi-agent core of learning, interaction and safety
12:00
Lunch + Social
13:30
Autonomous Racing Virtual Challenge: Contributed Talks
14:00
Spotlight Talks
14:30
Changliu Liu

Changliu Liu

CMU

Safe Learning, Prediction, and Coordination for Autonomous Driving
15:00
Justyna Zander

Justyna Zander

NVIDIA

Advanced simulation as a means for developing and validating autonomous vehicles at scale
15:30
Ding Zhao

Ding Zhao

CMU

Developing Trustworthy AI for Autonomous Driving
16:00
Break, Social, and Posters
17:00
Johannes Betz

Johannes Betz

UPenn

Learning to Handle Autonomous Vehicles at the Limit - Experiences from Roborace and the Indy Autonomous Challenge
17:30
Rowan McAllister

Rowan McAllister

TRI

Talk title TBD
18:00
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

Xinshuo Weng

Xinshuo Weng

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

Hitesh Arora

Hitesh Arora

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

Siddha Ganju

Siddha Ganju

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

Bingqing Chen

Bingqing Chen

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

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

Program Committee

  • Madhav Achar
  • Matthew Bauch
  • Manoj Bhat
  • Shravya Bhat
  • Wenhao Ding
  • Joe Fontaine
  • Sahika Genc
  • Shivam Goel
  • James Herman
  • Ruoxin Huang
  • Soonmin Hwang
  • Jennifer Isaza
  • Sidharth Kathpal
  • Anirudh Koul
  • Tanmay Kulkarni
  • Ankit Laddha
  • Jingyuan Li
  • Sharada Mohanty
  • Ingrid Navarro
  • Aarati Noronha
  • Alessandro Oltramari
  • Karthik Paga
  • Cameron Peron
  • Ehsan Qasemi
  • João Semedo
  • Aditya Sharma
  • Yash Shukla
  • Jivko Sinapov
  • Jayant Tamarapalli
  • Gyan Tatiya

Sponsors