| CARVIEW |
🚙BRAV🌍
roBustness and Reliability of Autonomous
Vehicles in the Open-world
An ICCV'23 workshop · October 3rd, 2023 · Paris, France
The BRAVO workshop presents a unique opportunity for researchers, industry experts, and policymakers to come together and address the critical challenge of trustworthy validation for autonomous vehicle systems on open roads.
The advances in artificial intelligence and computer vision propel the rise of highly automated ADAS and AVs, with the potential to revolutionize transportation and mobility services. However, deploying data-driven safety-critical systems with limited onboard resources and enduring guarantees on open roads remains a significant challenge.
To ensure safe deployment, ADAS/AVs must demonstrate the ability to navigate a wide range of driving conditions, including rare and dangerous situations, severe perturbations, and even adversarial attacks. Additionally, those capabilities must be ascertained to regulatory bodies, to secure certification, and to users, to earn their confidence.
The BRAVO workshop seeks to foster collaboration and innovation in developing tools and testbeds for assessing and enhancing the robustness, generalization power, transparency, and verification of computer vision models for ADAS/AVs. By working together, we can contribute to a safer, more efficient, and sustainable future for transportation.
We invite you to join us at the BRAVO workshop to explore solutions and contribute to developing reliable, robust computer vision for autonomous vehicles. Together, we can shape the future of transportation, ensuring safety and efficiency for all road users.
Keynote Speakers
Program
All quoted times refer to CEST.
Plase check the conference attendance details in advance, including the room assignments for the workshops.
Accepted Works
Workshop proceedings at TheCVF Open Access, IEEE Computer Society, and IEEE Xplore.
Poster session #1 (morning):
- A Glimpse at the First Results of the AutoBehave Project: a Multidisciplinary Approach to Evaluate the Usage of our Travel Time in Self-Driving Cars. Carlos F Crispim-Junior, Romain Guesdon, Christophe Jallais, Florent Laroche, Stephanie Souche-Le Corvec, Georges Beurier, Xuguang Wang, Laure Tougne Rodet. (Abstract)
- Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation. Dan Zhang, Kaspar Sakmann, William Beluch, Robin Hutmacher, Yumeng Li. (Full Paper, Poster)
- Camera-Based Road Snow Coverage Estimation. Kai Cordes, Hellward Broszio. (Full Paper, Poster)
- You Can Have Your Ensemble and Run It Too — Deep Ensembles Spread Over Time. Isak P Meding, Alexander Bodin, Adam Tonderski, Joakim Johnander, Christoffer Petersson, Lennart Svensson. (Full Paper)
- On the Interplay of Convolutional Padding and Adversarial Robustness. Paul Gavrikov, Janis Keuper. (Full Paper, Poster)
- Synthetic Dataset Acquisition for a Specific Target Domain. Joshua Niemeijer, Sudhanshu Mittal, Thomas Brox. (Full Paper)
- Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features. Travis Zhang, Katie Z Luo, Cheng Perng Phoo, Yurong You, Mark Campbell, Bharath Hariharan, Kilian Weinberger. (Full Paper)
- What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving. Jasmin Breitenstein, Florian Heidecker, Maria Lyssenko, Daniel Bogdoll, Maarten Bieshaar, Marius Zöllner, Bernhard Sick, Tim Fingscheidt. (Full Paper)
Poster session #2 (afternoon):
- A Subdomain-Specific Knowledge Distillation Method for Unsupervised Domain Adaptation in Adverse Weather Conditions. Yejin Lee, Gyuwon Choi, Donggon Jang, Daeshik Kim (Abstract, Poster)
- An Empirical Analysis of Range for 3D Object Detection. Neehar Peri, Mengtian Li, Benjamin Wilson, Yu-Xiong Wang, James Hays, Deva Ramanan. (Full Paper, Poster)
- Fusing Pseudo Labels with Weak Supervision for Dynamic Traffic Scenarios. Harshith Mohan Kumar, Sean Lawrence. (Abstract)
- GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data. Hongjae Lee, Changwoo Han, Jun-Sang Yoo, Seung-Won Jung. (Full Paper)
- Identifying Systematic Errors in Object Detectors with the SCROD Pipeline. Valentyn Boreiko, Matthias Hein, Jan Hendrik Metzen. (Full Paper)
- Introspection of 2D Object Detection using Processed Neural Activation Patterns in Automated Driving Systems. Hakan Y Yatbaz, Mehrdad Dianati, Konstantinos Koufos, Roger Woodman. (Full Paper)
- On Offline Evaluation of 3D Object Detection for Autonomous Driving. Tim Schreier, Katrin Renz, Andreas Geiger, Kashyap Chitta. (Full Paper)
- Sensitivity analysis of AI-based algorithms for autonomous driving on optical wavefront aberrations induced by the windshield. Dominik W Wolf, Markus Ulrich, Nikhil Kapoor. (Full Paper, Poster)
- T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals. James Giroux, Martin Bouchard, Robert Laganiere. (Full Paper)
Reviewers
We extend our warmest thanks to the team of reviewers who made this call for contributions possible:
...and three other reviewers who preferred to remain anonymous.
Call for Contributions
We invite participants to submit their work to the BRAVO Workshop as full papers or extended abstracts.
Full-Paper Submissions
Full papers must present original research, not published elsewhere, and follow the ICCV main conference format with a length of 4 to 8 pages (extra pages with references only are allowed). Supplemental materials are not allowed. Accepted full papers will be included in the conference proceedings.
Extended Abstract Submissions
We welcome extended abstracts, which may serve works of a more speculative or preliminary nature that may not be fit for a full-length paper. Authors are also welcome to submit extended abstracts for previously or concomitantly published works that could foster the workshop objectives.
Extended abstracts must have no more than 1000 words, in addition to a single illustration and references. We suggest authors use the extended abstract template provided.
Accepted extended abstracts will be presented without inclusion in the proceedings.
Topics of Interest
The workshop welcomes submissions on all topics related to robustness, generalization, transparency, and verification of computer vision for autonomous driving systems. Topics of interest include but are not limited to:
- Robustness & Domain Generalization
- Domain Adaptation & Shift
- Long-tail Recognition
- Perception in Adverse Conditions
- Out-of-distribution Detection
- Applications of Uncertainty Quantification
- Monitoring, Failure Prediction & Anomaly Detection
- Confidence Calibration
- Image Enhancement Techniques
Guidelines
All submissions must be made through the CMT system, before the deadline.
The BRAVO Workshop reviewing is double-blind. Authors of all submissions must follow the main conference policy on anonymity. We encourage authors to follow the ICCV 2023 Suggested Practices for Authors, except in what concerns supplemental material, which is not allowed.
While we encourage reproducibility, we welcome preliminary/speculative works where source codes or data might need more time until broad disclosure. We still expect evidence of ethics clearance if the submission uses novel data sources from human subjects.
BRAVO Workshop reviewers must follow the ICCV 2023 Ethics Guidelines for Reviewers. We encourage reviewers to follow the ICCV 2023 Reviewer Guidelines, and Tips to Write Good Reviews.
Camera-ready instructions
The submission guidelines are detailed here.
Posters
We will organize two poster sessions, in the morning and afternoon, inside the workshop room. All accepted works will be assigned to one of the poster sessions, including those selected for the oral spotlights.
The poster size for workshops differs from the main conference's. The panel size will be 95.4 cm wide x 138.8 cm tall (aspect ratio 0.69:1). A0 paper in portrait orientation will fit the panel with some margin.The ICCV organizers partnered with an on-site printing service from which you may collect your printed poster: more information at the main conference attendance info site.
Important Dates
BRAVO Challenge
In conjunction with the Workshop on Uncertainty Quantification for Computer Vision, we are organizing a challenge on the robustness of autonomous driving in the open world. The 2024 BRAVO Challenge aims at benchmarking segmentation models on urban scenes undergoing diverse forms of natural degradation and realistic-looking synthetic corruptions.
For more information, please check the BRAVO Challenge Repository and the Challenge Task Website at ELLIS/ELSA.
Acknowledgements
We extend our heartfelt gratitude to the authors of ACDC, SegmentMeIfYouCan and Out-of-context Cityscapes for generously granting us permission to repurpose their benchmarking data. We are also thankful to the authors of GuidedDisent and Flare Removal for providing the amazing toolboxes that helped synthesize realistic-looking raindrops and light flares. All those people collectively contributed to creating BRAVO, a unified benchmark for robustness in autonomous driving.
We are excited to unveil the BRAVO Challenge as an initiative within ELSA — European Lighthouse on Secure and Safe AI, a network of excellence funded by the European Union. The BRAVO Challenge is officially featured on the ELSA Benchmarks website as the Autonomous Driving/Robust Perception task.
Organizers
Supported by
Original photo by Kai Gradert on Unsplash, modified to illustrate stable diffusion augmentations.