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| # Dataset Card for nuScenes-Atk |
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| ## Dataset Description |
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| - **Dataset Name:** nuScenes-Atk |
| - **Overview:** |
| The Adv-nuSc dataset is a collection of adversarial driving scenarios generated by the SCS-PE 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 such as cut-ins, sudden lane changes, tailgating, and blind spot intrusions. |
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| - **Affiliation:** SUN YAT-SEN University |
| - **License:** [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/) |
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| ## Dataset Structure |
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| - **Scenes:** 150 (6,019 samples), each 20 seconds long |
| - **Data Types:** |
| - Multiview video data from 6 camera perspectives |
| - 3D bounding box annotations for all objects |
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| - **Key Statistics:** |
| - 10538 instances |
| - 247,548 ego poses |
| - 184,209 total annotations |
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| ## Usage |
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| - The nuScenes-Atk dataset follows the **nuScenes format**. |
| - Minor modifications may be needed to evaluate common end-to-end autonomous driving models. |
| - Please follow the instructions in **Eval E2E** for integration. |
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| ## Creation Process |
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| ### Source Data |
| - Built upon the **nuScenes validation set** (150 scenes) |
| - Uses nuScenes’ original sensor data and annotations as foundation |
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| ### Source Data |
| - 1.**Potential Risk Monitoring and Detection**: Obtain the consecutive segments with the highest potential risk by using the sliding window method and evaluate the vehicle with the greatest potential risk to the ego. |
| - 2.**Trajectory Generation**: The two-stage "decision-making + control" strategy is utilized to control the agent to perform risky behaviors on the ego vehicle while ensuring compliance with driving regulations. |
| - 3.**Neural Rendering**: Produces photorealistic multiview videos using MagicDrive-V2 |
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| ### Filtering |
| Scenarios are filtered to ensure: |
| - No collisions between adversarial and other vehicles |
| - Adversarial vehicle remains within a **100m × 100m** area around ego |
| - Meaningful interaction with ego vehicle occurs |
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| ## Intended Use |
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| - **Primary Purpose:** Robustness evaluation of autonomous driving systems |
| - **Applications:** |
| - Stress-testing end-to-end AD models (e.g., UniAD, VAD, SparseDrive, DiffusionDrive) |
| - Identifying failure modes in perception, prediction, and planning modules |
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| ## Limitations |
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| - Focuses on **single adversarial vehicles** (extendable to multiple) |
| - **Open-loop evaluation** (no reactive ego agent) |
| - Minor rendering artifacts compared to real sensor data |
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| ## Ethical Considerations |
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| ### Safety |
| - Intended **for research use in controlled environments only** |
| - Should **not** be used to train real-world systems without additional safety validation |
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| ### Privacy |
| - Based on **nuScenes data** which has already undergone anonymization |
| - No additional privacy concerns introduced by generation process |
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