Participation Instructions - Autonomous Driving

For detailed  instructions, please refer to the task repository in: https://github.com/valeoai/bravo_challenge

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

Track 1 - Single-domain training

In this track, you must train your models exclusively on the Cityscapes dataset. This track evaluates the robustness of models trained with limited supervision and geographical diversity when facing unexpected corruptions observed in real-world scenarios.

Track 2 - Multi-domain training

In this track, you must train your models over a mix of datasets, whose choice is strictly limited to the list provided below, comprising both natural and synthetic domains. This track assesses the impact of fewer constraints on the training data on robustness.

List of accepted datasets:

• Cityscapes
• BDD100k
• Mapillary Vistas
• India Driving Dataset
• WildDash 2
• (synthetic) GTA5 Dataset
• (synthetic) SHIFT Dataset

General rules

1. The task is semantic segmentation, with pixel-wise evaluation performed on the 19 semantic classes of Cityscapes.
2. Models in each track must be trained using only the datasets allowed for that track.
3. Employing generative models for synthetic data augmentation is strictly forbidden.
4. All results must be reproducible. Participants must submit a white paper containing comprehensive technical details alongside their results. Participants must make models and inference code accessible.
5. Evaluation will consider the 19 classes of Cityscapes (see below).
6. Teams must register a single account for submitting to the evaluation server. An organization (e.g. a University) may have several teams with independent accounts only if the teams are not cooperating.