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.
In conjunction with the BRAVO workshop at ICCV'23, we are organizing a challenge on the topic of robustness of autonomous driving in the open-world. The 2023 BRAVO challenge aims at benchmarking segmentation models on urban scenes undergoing diverse forms of natural degradation and realistic looking synthetic corruptions. We offer two tracks for benchmarking segmentation models trained on a single dataset and on multiple heterogenous datasets.
For any question about this challenge, please contact firstname.lastname@example.org