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Datasets:
TUM-AVS
/
Nurisk-ICRA2026

Tasks:
Question Answering
Image Classification
Modalities:
Image
Text
Formats:
parquet
Size:
10K - 100K
ArXiv:
Tags:
vqa
autonomous-driving
risk-assessment
bev
computer-vision
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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+ ---
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+ title: Nurisk
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+ datasets:
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+ - Yuan-avs/Nurisk
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+ library_name: datasets
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+ tags:
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+ - vqa
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+ - autonomous-driving
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+ - risk-assessment
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+ - bev
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+ - computer-vision
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+ pretty_name: Nurisk
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+ task_categories:
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+ - question-answering
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+ - image-classification
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+ license: mit
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+ ---
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+
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+ # Nurisk: VQA for Risk Assessment in Autonomous Driving
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+
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+ Nurisk is a visual question answering dataset focusing on risk assessment for autonomous driving. Each row contains:
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+
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+ - image: a BEV image
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+ - question: a driving-related question
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+ - answer: the ground truth answer
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+
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+ ## Paper
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+
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+ NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving — see the paper on [arXiv:2509.25944 ](https://www.arxiv.org/abs/2509.25944).
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+
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+ ## Framework
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+
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+ ![NuRisk Framework](assets/Framework.png)
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+
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+ ## Dataset Structure
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+
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+ - Splits: `train`, `validation`
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+ - Columns: `image`, `question`, `answer`
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+
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+ ## Usage
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ ds = load_dataset("Yuan-avs/Nurisk")
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+ sample = ds["train"][0]
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+ print(sample["question"]) # text
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+ print(sample["answer"]) # text
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+ img = sample["image"] # PIL.Image.Image
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+ img.show()
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+ ```
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+
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+ ## Notes
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+
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+ - Images may be referenced multiple times across different questions.
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+ - The dataset viewer only exposes `image`, `question`, and `answer`.
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+
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+ ## License
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+
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+ Please specify the license applicable to the images and annotations.
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+
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+ ## Citation
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+
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+ If you use this dataset, please cite the authors accordingly.
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+
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+ @article{gao2025nurisk,
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+ title={NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving},
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+ author={Gao, Yuan and Piccinini, Mattia and Brusnicki, Roberto and Zhang, Yuchen and Betz, Johannes},
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+ journal={arXiv preprint arXiv:2509.25944} ,
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+ year={2025}
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+ }
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+
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+ If you are interested in foundation model-based scenario generation and scenario analysis, you may also refer to our comprehensive survey, which provides
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+ the first overview of this emerging research area.
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+
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+ Publication details:
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+ 📅 June 2025 – Released on arXiv.
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+ 📊 The accompanying repository categorizes 342 papers, including:
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+ 93 on scenario generation
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+ 54 on scenario analysis
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+ 55 on datasets
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+ 21 on simulators
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+ 25 on benchmark challenges
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+ 94 on other related topics
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+
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+ 📄 Paper: https://arxiv.org/abs/2506.11526
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+
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+ 💻 GitHub: https://github.com/TUM-AVS/FM-AD-Survey
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