About
Welcome to the Learn-to-Race Autonomous Racing Virtual Challenge on Artificial Intelligence for Autonomous Driving.
As autonomous technology approaches maturity, it is of paramount importance for autonomous vehicles to adheres to safety specifications, whether in urban driving or high-speed racing. Racing demands each vehicle to drive at its physical limits with barely any margin for safety, when any infraction could lead to catastrophic failures. Given this inherent tension, we envision autonomous racing to serve as a particularly challenging proving ground for safe learning algorithms.
We propose the Safe Learning for Autonomous Driving workshop, as a venue for research towards achieving the safety benefits of autonomous vehicles, supplemented by standardized evaluation in a high-fidelity racing environment. Participants can choose to take part in the Challenge by competing for top leaderboard positions and/or by submitting articles to one of three conference paper tracks.
Dates
Paper Submission
Submissions open: 2 March 2022
Submissions due: 13 May 2022
Reviewing starts: 14 May 2022
Reviewing ends: 30 May 2022
Notification: 3 June 2022
Camera Ready + Video upload: 17 June 2022
Challenge
Open: TBD
Close: TBD
Winners notification: 21 February 2022
Workshop
Event: 23 July 2022
Speakers
Max Kumskoy
Head of Automated Driving Systems
ARRIVAL
Sahika Genc
Principal Scientist
Amazon AWS
Jaime Fisac
Assistant Professor
Princeton University
Johannes Betz
Postdoctoral Researcher
University of Pennsylvania
Justyna Zander
Global Head of Verification and Validation for Autonomous Driving
NVIDIA
Ding Zhao
Assistant Professor
Carnegie Mellon University
Changliu Liu
Assistant Professor
Carnegie Mellon University
Rowan McAllister
Machine Learning Scientist
Toyota Research Institute
David Held
Assistant Professor
Carnegie Mellon University
Challenge
The Learn-to-Race Autonomous Racing Virtual Challenge is now active. Participate now on AICrowd!
Organisers
Jonathan Francis
PhD candidate at CMU, Research Scientist at Bosch; domain knowledge-enhanced representation learning, applied to robotics and 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, focusing on 3D computer vision and generative models for autonomous systems
Daniel Omeiza
PhD student at Oxford, focusing on explainable AI and decision-making, in autonomous driving
Siddha Ganju
Researcher and Data Scientist at NVIDIA, focusing on computer vision optimization for vehicle autonomy and medical instruments
Hitesh Arora
Researcher at Amazon, focusing on multimodal perception and reinforcement learning, applied to 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
Program Committee
- Madhav Achar
- Matthew Bauch
- Manoj Bhat
- Shravya Bhat
- Joe Fontaine
- Sahika Genc
- Shivam Goel
- James Herman
- Ruoxin Huang
- Soonmin Hwang
- Jennifer Isaza
- Sidharth Kathpal
- Ankit Laddha
- Jingyuan Li
- Sharada Mohanty
- Ingrid Navarro
- Aarati Noronha
- Karthik Paga
- Cameron Peron
- Joao Semedo
- Aditya Sharma
- Yash Shukla
- Jivko Sinapov
- Gyan Tatiya
- Weiran Yao
- Xinjie Yao
Sponsors
