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license: cc-by-sa-4.0 |
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# Dataset Card for Adv-nuSc |
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## Dataset Description |
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### Overview |
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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. |
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- **Curated by**: Zhiyuan Xu, Bohan Li, Huan-ang Gao, Mingju Gao, Yong Chen, Ming Liu, Chenxu Yan, Hang Zhao, Shuo Feng, Hao Zhao |
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- **Affiliation**: Tsinghua University; Geely Auto |
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- **License**: CC-BY-SA-4.0 |
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- **Project Page**: [Challenger](https://pixtella.github.io/Challenger/) |
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### Dataset Structure |
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The dataset consists of: |
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- **156 scenes** (6,115 samples), each 20 seconds long |
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- **Multiview video data** from 6 camera perspectives |
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- **3D bounding box annotations** for all objects |
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Key statistics: |
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- 12,858 instances |
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- 254,436 ego poses |
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- 225,085 total annotations |
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### Usage |
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The Adv-nuSc 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](https://github.com/Pixtella/Challenger#6-evaluating-end-to-end-autonomous-driving-models-on-the-generated-adversarial-dataset). |
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## Creation Process |
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### Source Data |
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- Built upon the nuScenes validation set (150 scenes) |
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- Uses nuScenes' original sensor data and annotations as foundation |
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### Adversarial Generation |
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1. **Trajectory Generation**: Uses diffusion models to create diverse adversarial maneuvers |
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2. **Physics Simulation**: Simulates trajectories with an LQR controller and kinematic bicycle model |
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3. **Multi-round Refinement**: Iteratively improves trajectories using: |
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- Drivable area compliance checks |
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- Collision avoidance |
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- Adversarial challenge scoring |
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4. **Neural Rendering**: Produces photorealistic multiview videos using MagicDriveDiT |
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### Filtering |
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Scenarios are filtered to ensure: |
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- No collisions between adversarial and other vehicles |
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- Adversarial vehicle remains within 100m × 100m area around ego |
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- Meaningful interaction with ego vehicle occurs |
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## Intended Use |
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- **Robustness evaluation** of autonomous driving systems |
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- **Stress-testing** end-to-end AD models (e.g., UniAD, VAD, SparseDrive, DiffusionDrive) |
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- **Identifying failure modes** in perception, prediction, and planning modules |
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### Limitations |
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- Currently focuses on single adversarial vehicles (though extendable to multiple) |
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- Open-loop evaluation (no reactive ego agent) |
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- Minor rendering artifacts compared to real sensor data |
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## Ethical Considerations |
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### Safety |
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- Intended for research use in controlled environments only |
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- Should not be used to train real-world systems without additional safety validation |
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### Privacy |
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- Based on nuScenes data which has already undergone anonymization |
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- No additional privacy concerns introduced by generation process |
