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README.md

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-license: cc-by-sa-4.0
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+license: cc-by-sa-4.0
+---
+
+# Dataset Card for Adv-nuSc
+
+## 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.
+
+- **Curated by**: Zhiyuan Xu, Bohan Li, Huan-ang Gao, Mingju Gao, Yong Chen, Ming Liu, Chenxu Yan, Hang Zhao, Shuo Feng, Hao Zhao
+- **Affiliation**: Tsinghua University; Geely Auto
+- **License**: CC-BY-SA-4.0
+- **Project Page**: [Challenger](https://pixtella.github.io/Challenger/)
+
+### Dataset Structure
+
+The dataset consists of:
+- **156 scenes** (6,115 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 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).
+
+## Creation Process
+
+### Source Data
+- Built upon the nuScenes validation set (150 scenes)
+- Uses nuScenes' original sensor data and annotations as foundation
+
+### Adversarial Generation
+1. **Trajectory Generation**: Uses diffusion models to create diverse adversarial maneuvers
+2. **Physics Simulation**: Simulates trajectories with an LQR controller and kinematic bicycle model
+3. **Multi-round Refinement**: Iteratively improves trajectories using:
+   - Drivable area compliance checks
+   - Collision avoidance
+   - Adversarial challenge scoring
+4. **Neural Rendering**: Produces photorealistic multiview videos using MagicDriveDiT
+
+### 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