The just better DATAset is a multimodal dataset designed to support robust validation and analysis of perception systems in safety‑critical driving scenarios. The dataset has been curated with a strong focus on underrepresented, rare, and safety‑relevant situations, addressing known gaps in conventional real‑world driving data.

Dataset Overview

The dataset consists of three data batches, each collected using three independent test vehicles from the following project partners:

• AVL Deutschland GmbH (AVL)
• b-plus technologies GmbH (b-plus)
• FZI Forschungszentrum Informatik (FZI)

equipped with heterogeneous multimodal sensor setups, including:

• LiDAR
• Radar
• Camera

This setup enables comprehensive multimodal perception research and provides realistic sensor diversity reflective of real‑world deployments.

The metadata for the real-world data is provided as ASAM OpenLABEL files. This also enables the seamless use of the various data batches thanks to a standardized description format.

• Meta data as ASAM OpenLABEL

Focus on Safety‑Critical and Long‑Tail Scenarios

Unlike generic driving datasets, the jbDATAset explicitly targets challenging and safety‑critical scenarios, including but not limited to:

• Unknown or uncommon objects on the road
• Diverse and adverse weather conditions
• Situations that are typically underrepresented in large‑scale fleet data

This makes the dataset particularly well‑suited for validation, robustness testing, and failure analysis of perception models.

Hybrid Real & Synthetic Data Design

In order to further improve the distribution of the real-world data, the dataset adopts a hybrid strategy and supplements the jbDATAset with purpose-built synthetic data:

• Synthetic Data Batch from AUMOVIO

The synthetic components are not intended to replace real data, but to systematically complement it, enabling controlled variation, improved coverage of rare scenarios, and targeted stress testing. This hybrid approach is especially valuable for model validation and generalization assessment.

Ground Truth and Pseudolabels

To support different stages of development and evaluation, the dataset provides:

• High‑quality ground truth annotations for selected samples
• Pseudolabels for other parts of the dataset

This reflects realistic industrial workflows and allows users to study:

• Model performance under varying label fidelity
• The impact of pseudolabeling in validation and benchmarking pipelines
• Strategies for combining ground truth and weak supervision

Intended Use

The jbDATAset is designed primarily for:

• Validation and robustness evaluation
• Safety‑oriented perception research
• Analysis of long‑tail and rare scenarios
• Benchmarking multimodal perception systems
• Research on hybrid real‑synthetic data strategies

It is not intended as a generic training dataset, but as a high‑value asset for testing, analysis and validation, particularly in safety‑critical contexts.

Publication

The consortium of just better DATA will publish and present a paper about the jbDATAset within the Curated Data for Efficient Learning Workshop at the ECCV 2026 in Malmö (September 8 – 12th, 2026). Once the paper is published it will be linked here.