Overview - Autonomous Driving

Autonomous vehicles are safety-critical systems operating in a widely complex open world; as such, they must not only deliver excellent performance in their operational design domain (ODD), but also be provably robust to all unexpected inputs caused by adversarial attacks, extreme weather conditions, changes of operation domains or rare but potentially catastrophic driving situations. We aim at developing testbeds to assess the robustness of driving perception models, so that it can be statistically proven. Relying on existing (possibly, augmented) or new datasets if suited, benchmarks with associated baselines will be set up to address the following safety-centered challenges: a) calibration of models' outputs and estimation of their uncertainty; b) detection of out-of-domain inputs (either at scene or object level); c) assessment of gradual domain shifts away from the intended ODD.

The BRAVO Challenge aims to benchmark semantic segmentation models on urban scenes undergoing diverse forms of natural degradation and realistic-looking synthetic corruption.

We promote the 2024 BRAVO Challenge in conjunction with the 3rd Workshop on Uncertainty Quantification for Computer Vision @ ECCV 2024.

We promote the 2025 BRAVO Challenge in conjunction with the 4rd Workshop on Uncertainty Quantification for Computer Vision @ CVPR 2025.

Contact information

Participants with inquires on the challenge data, code, rules, metrics, etc., please use the ticketing system of the challenge repository.

For other questions about the challenge, please contact tuan-hung.vu@valeo.com