---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---

# Dataset Card for nuScenes-Atk

## Dataset Description

- **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.

- **Affiliation:** SUN YAT-SEN University 
- **License:** [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)

---

## Dataset Structure

- **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  

- **Key Statistics:**  
  - 10538 instances  
  - 247,548 ego poses  
  - 184,209 total annotations  

---

## Usage

- 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.

---

## Creation Process

### Source Data
- Built upon the **nuScenes validation set** (150 scenes)  
- Uses nuScenes’ original sensor data and annotations as foundation  

### 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

### 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  

---

## Intended Use

- **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  

---

## Limitations

- Focuses on **single adversarial vehicles** (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  

---