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license: cc-by-sa-4.0
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# 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