title: ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving
    Systems
summary: Recent advancements in Large Multimodal Models (LMMs) have shown promise in
  Autonomous Driving Systems (ADS). However, their direct application to ADS is
  hindered by challenges such as misunderstanding of traffic knowledge, complex
  road conditions, and diverse states of vehicle. To address these challenges, we
  propose the use of Knowledge Editing, which enables targeted modifications to a
  model's behavior without the need for full retraining. Meanwhile, we introduce
  ADS-Edit, a multimodal knowledge editing dataset specifically designed for ADS,
  which includes various real-world scenarios, multiple data types, and
  comprehensive evaluation metrics. We conduct comprehensive experiments and
  derive several interesting conclusions. We hope that our work will contribute
  to the further advancement of knowledge editing applications in the field of
  autonomous driving. Code and data are available in
  https://github.com/zjunlp/EasyEdit.
paper_authors._id: 67e4b0b850ca38886d7e78d0
paper_authors.name: Chenxi Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:20:08.223Z
paper_authors._id: 6466549fa1a19b0623fe868f
paper_authors.avatarUrl: /avatars/8f8c81e3db782df2b47833ee61d845e2.svg
paper_authors.fullname: Chenxi Wang
paper_authors.type: user
paper_authors.user: Chenxiwang
paper_authors._id: 67e4b0b850ca38886d7e78d1
paper_authors.name: Jizhan Fang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:20:16.102Z
paper_authors._id: 669663472d25bd04e9af1d66
paper_authors.avatarUrl: /avatars/8b11d5d79d1b8b205baa498a942f573c.svg
paper_authors.fullname: Jizhan Fang
paper_authors.type: user
paper_authors.user: JizhanFang
paper_authors._id: 67e4b0b850ca38886d7e78d2
paper_authors.name: Xiang Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:20:23.885Z
paper_authors._id: 64c073417255ff868721fb54
paper_authors.avatarUrl: /avatars/c2a00114598aa89f071b937f0af4077f.svg
paper_authors.fullname: Xiang CHEN
paper_authors.type: user
paper_authors.user: xiangchen-dvi
paper_authors._id: 67e4b0b850ca38886d7e78d3
paper_authors.name: Bozhong Tian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:20:29.614Z
paper_authors._id: 626e9144afb8cb8f7a5cb503
paper_authors.avatarUrl: /avatars/7e9df47dd4e3cb6df193878e561cc2bc.svg
paper_authors.fullname: Bozhong Tian
paper_authors.type: user
paper_authors.user: bozhong
paper_authors._id: 67e4b0b850ca38886d7e78d4
paper_authors.name: Ziwen Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:20:40.540Z
paper_authors._id: 6549caee44e75a7de4fee2fa
paper_authors.avatarUrl: /avatars/5aea69671eb1299aaaa948d888b4b64f.svg
paper_authors.fullname: Xu Ziwen
paper_authors.type: user
paper_authors.user: xzwnlp
paper_authors._id: 67e4b0b850ca38886d7e78d5
paper_authors.name: Huajun Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:20:55.588Z
paper_authors._id: 67c06592ebc2e682b2628bde
paper_authors.avatarUrl: /avatars/1032c0319eded956d2dd461b0e1b4ddb.svg
paper_authors.fullname: Huajun Chen
paper_authors.type: user
paper_authors.user: HuajunChen
paper_authors._id: 67e4b0b850ca38886d7e78d6
paper_authors.name: Ningyu Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-27T10:21:07.281Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_title: ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving
    Systems
paper_summary: Recent advancements in Large Multimodal Models (LMMs) have shown promise in
  Autonomous Driving Systems (ADS). However, their direct application to ADS is
  hindered by challenges such as misunderstanding of traffic knowledge, complex
  road conditions, and diverse states of vehicle. To address these challenges, we
  propose the use of Knowledge Editing, which enables targeted modifications to a
  model's behavior without the need for full retraining. Meanwhile, we introduce
  ADS-Edit, a multimodal knowledge editing dataset specifically designed for ADS,
  which includes various real-world scenarios, multiple data types, and
  comprehensive evaluation metrics. We conduct comprehensive experiments and
  derive several interesting conclusions. We hope that our work will contribute
  to the further advancement of knowledge editing applications in the field of
  autonomous driving. Code and data are available in
  https://github.com/zjunlp/EasyEdit.
paper_ai_summary: Knowledge Editing and ADS-Edit enhance Large Multimodal Models for Autonomous Driving Systems through targeted modifications and comprehensive real-world testing.
paper_ai_keywords: Large Multimodal Models Autonomous Driving Systems Knowledge Editing ADS-Edit multimodal knowledge editing real-world scenarios