| --- |
| pretty_name: Dataset Card for nuScenes-Atk |
| --- |
| Dataset Description |
| Overview |
| The Adv-nuSc dataset is a collection of adversarial driving scenarios generated by the Challenger framework, designed to evaluate the robustness of autonomous driving (AD) systems. It builds upon the nuScenes validation set, introducing intentionally challenging interactions that stress-test AD models with aggressive maneuvers like cut-ins, sudden lane changes, tailgating, and blind spot intrusions. |
|
|
| Affiliation: SUN YAT-SEN University; Geely Auto |
| License: CC-BY-SA-4.0 |
|
|
| Dataset Structure |
| The dataset consists of: |
| 150 scenes (6,019 samples), each 20 seconds long |
| Multiview video data from 6 camera perspectives |
| 3D bounding box annotations for all objects |
| Key statistics: |
|
|
| 12,858 instances |
| 254,436 ego poses |
| 225,085 total annotations |
|
|
| Usage |
|
|
| The nuScenes-Atk dataset is in nuScenes format. However, a few minor modifications are needed to evaluate common end-to-end autonomous driving models on it. Please follow instructions in Eval E2E. |
|
|
| Creation Process |
|
|
| Source Data |
|
|
| Built upon the nuScenes validation set (150 scenes) |
| Uses nuScenes' original sensor data and annotations as foundation |
|
|
| Filtering |
|
|
| Scenarios are filtered to ensure: |
|
|
| No collisions between adversarial and other vehicles |
| Adversarial vehicle remains within 100m × 100m area around ego |
| Meaningful interaction with ego vehicle occurs |
| |
| Intended Use |
|
|
| Robustness evaluation of autonomous driving systems |
| Stress-testing end-to-end AD models (e.g., UniAD, VAD, SparseDrive, DiffusionDrive) |
| Identifying failure modes in perception, prediction, and planning modules |
|
|
| Limitations |
|
|
| Currently focuses on single adversarial vehicles (though extendable to multiple) |
| Open-loop evaluation (no reactive ego agent) |
| Minor rendering artifacts compared to real sensor data |
|
|
| Ethical Considerations |
|
|
| Safety |
|
|
| Intended for research use in controlled environments only |
| Should not be used to train real-world systems without additional safety validation |
| |
| Privacy |
|
|
| Based on nuScenes data which has already undergone anonymization |
| No additional privacy concerns introduced by generation process |
