The jbDATA Synthetic Dataset contains 10,000 automatically annotated synthetic driving images generated to complement real-world sensor recordings with scenarios that are difficult to acquire and balance through conventional data collection alone. Developed within the jbDATA Smart Data Loop framework, the dataset targets underrepresented operating conditions relevant to autonomous driving, with a particular focus on adverse weather, including rain and snow, challenging illumination, nighttime scenes, tunnels, and other situations that can impact perception performance and robustness. The synthetic data was created as part of a targeted data-enrichment process guided by coverage and data-gap analysis, supporting the development and evaluation of AI systems under challenging environmental conditions. The release is provided in the widely adopted MS COCO format with object-level annotations, enabling straightforward integration into existing computer vision validation and benchmarking workflows.
This dataset is part of a broader collection of datasets being released within the project - follow the justbetterData website.