{
 "id": "Yuan-avs/Nurisk",
 "owner": "Yuan-avs",
 "name": "Nurisk",
 "downloads": 1251,
 "likes": 4,
 "last_modified": "2026-05-29T12:33:22.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "question-answering",
  "image-classification"
 ],
 "citation": "If you use this dataset, please cite the authors accordingly.\n\n@article{gao2025nurisk,\n  title={NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving},\n  author={Gao, Yuan and Piccinini, Mattia and Brusnicki, Roberto and Zhang, Yuchen and Betz, Johannes},\n  journal={arXiv preprint arXiv:2509.25944} ,\n  year={2025}\n}\n\nIf you are interested in foundation model-based scenario generation and scenario analysis, you may also refer to our comprehensive survey, which provides \nthe first overview of this emerging research area.\n\nPublication details:\n📅 June 2025 – Released on arXiv.\n📊 The accompanying repository categorizes 342 papers, including:\n93 on scenario generation\n54 on scenario analysis\n55 on datasets\n21 on simulators\n25 on benchmark challenges\n94 on other related topics\n\n📄 Paper: https://arxiv.org/abs/2506.11526\n\n💻 GitHub: https://github.com/TUM-AVS/FM-AD-Survey",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "a1f2eed8daacfa822644550475df631e91127a7f",
 "stated_evidence": "https://huggingface.co/datasets/Yuan-avs/Nurisk/blob/a1f2eed8daacfa822644550475df631e91127a7f/README.md",
 "file_evidence": "https://huggingface.co/datasets/Yuan-avs/Nurisk/tree/a1f2eed8daacfa822644550475df631e91127a7f",
 "card_nc": false
}