Datasets:

Pixtella
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Adv-nuSc

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Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 299, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 353, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 304, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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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

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.

Creation Process

Source Data

Built upon the nuScenes validation set (150 scenes)
Uses nuScenes' original sensor data and annotations as foundation

Adversarial Generation

Trajectory Generation: Uses diffusion models to create diverse adversarial maneuvers
Physics Simulation: Simulates trajectories with an LQR controller and kinematic bicycle model
Multi-round Refinement: Iteratively improves trajectories using:
- Drivable area compliance checks
- Collision avoidance
- Adversarial challenge scoring
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

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