About the Workshop
This workshop aims to foster communication around current paradigms for autonomous vehicles. While we see sufficient maturity in all parts of the classical self-driving stack, their combined performance leaves room for improvement. In the past years, ML-first solutions have been showing promising results, not only in simulation or simplified scenarios, but also in real-world, complex environments. We want to open up the discussion on the challenges that need to be solved in order to enable the next generation of AVs and to encourage new ideas regarding their scalability and safety.
Call for Papers
We invite submissions to the Fresh Perspectives on the Future of Autonomous Driving workshop, hosted at ICRA 2022.
A non-exhaustive list of relevant topics:
Submission Portal: CMT
Dual Submission: We accept dual submissions, but the manuscript must contain substantial original contents not submitted to prior conferences, workshop, or journal.
Non-archival: The workshop is a non-archival venue and will not have official proceedings. Workshop submissions can be subsequently or concurrently submitted to other venues.
Visibility: Submissions and reviews will be private. Only accepted papers will be made public.
Review and Selection
The review process will be double-blind. As an author, you are responsible for anonymizing your submission. You should not include author names, author affiliations, or acknowledgements in your submission.
All accepted papers will be presented as posters. The guidelines for the posters are the same as at the main conference.
At least one co-author of each accepted paper is expected to register for ICRA 2022 and attend the poster session. Remote attendance permitted.
If you need a Visa to enter the US, please apply for one as early as possible. See Visa information provided on the ICRA website.
All the accepted submissions will be available on our workshop website, though authors could indicate explicitly if they want to opt out.
Extended paper submission deadline : April 25th, 2022 (anywhere on earth)
Notification to authors: May 9th, 2022
Information for authors of accepted papers
Posters: Authors should prepare a poster to be presented at the conference.
The ICRA 2022 poster guidelines suggest a poster no larger than 60" wide and 40" tall (1.5m wide x 1m tall).
The following are some poster printing options near the Pennsylvania Convention Center:
FedEx Office Print & Ship Center, located at 1201 Market St, Philadelphia, PA 19107, phone number: +1 (215) 923-2520
Media Copy, located at 1315 Walnut St # 1, Philadelphia, PA 19107, phone number: +1 (215) 717-5151
Improving Efficiency of Attention-based Models for Motion Prediction
Marcos Conde, Miguel Eduardo Ortiz Huamani, Carlos Gómez-Huélamo
Sociotechnical Specification for the Broader Impacts of Autonomous Vehicles
Thomas Gilbert, Aaron Snoswell, Michael Dennis, Rowan McAllister, Cathy Wu
Uncertainty estimation for Cross-dataset performance in Trajectory prediction
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde
Coordinated Multi-Agent Motion Planning via Imitation Learning
Andrei Ivanovic, Rowan McAllister, Ashkan Mirzaei, Igor Gilitschenski
Towards Transferable Interactive Policies for Automated Driving with Hidden Parameter Block MDPs
Danial Kamran, Etienne Bührle
Parametric Control Barrier Functions based Adaptive Safe Control for Heterogeneous Autonomous Vehicles
Yiwei Lyu, Wenhao Luo, John Dolan
Is Anyone There? Learning a Planner Contingent on Perceptual Uncertainty
Charles Packer, Nicholas Rhinehart, Rowan McAllister, Matthew Wright, Xin Wang, Jeff He, Sergey Levine, Joseph Gonzalez
Learning and Predicting Multimodal Vehicle Action Distributions in a Unified Probabilistic Model Without Labels
Charles Richter, Patrick Barragan, Sertac Karaman
Estimation of Appearance and Occupancy Information in Bird’s Eye View from Surround Monocular Images
Sarthak Sharma, Unnikrishnan Nair, Udit Singh Parihar, Midhun Menon, Srikanth Vidapanakal
LiDAR-as-Camera for End-to-End Driving
Ardi Tampuu, Tambet Matiisen, Romet Aidla
Group Distributionally Robust Reinforcement Learning
Mengdi Xu, Peide Huang, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao