ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving
  Systems
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.
67e4b0b850ca38886d7e78d0
Chenxi Wang
admin_assigned
2025-03-27T10:20:08.223Z
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/avatars/8f8c81e3db782df2b47833ee61d845e2.svg
Chenxi Wang
user
Chenxiwang
67e4b0b850ca38886d7e78d1
Jizhan Fang
admin_assigned
2025-03-27T10:20:16.102Z
669663472d25bd04e9af1d66
/avatars/8b11d5d79d1b8b205baa498a942f573c.svg
Jizhan Fang
user
JizhanFang
67e4b0b850ca38886d7e78d2
Xiang Chen
admin_assigned
2025-03-27T10:20:23.885Z
64c073417255ff868721fb54
/avatars/c2a00114598aa89f071b937f0af4077f.svg
Xiang CHEN
user
xiangchen-dvi
67e4b0b850ca38886d7e78d3
Bozhong Tian
admin_assigned
2025-03-27T10:20:29.614Z
626e9144afb8cb8f7a5cb503
/avatars/7e9df47dd4e3cb6df193878e561cc2bc.svg
Bozhong Tian
user
bozhong
67e4b0b850ca38886d7e78d4
Ziwen Xu
admin_assigned
2025-03-27T10:20:40.540Z
6549caee44e75a7de4fee2fa
/avatars/5aea69671eb1299aaaa948d888b4b64f.svg
Xu Ziwen
user
xzwnlp
67e4b0b850ca38886d7e78d5
Huajun Chen
admin_assigned
2025-03-27T10:20:55.588Z
67c06592ebc2e682b2628bde
/avatars/1032c0319eded956d2dd461b0e1b4ddb.svg
Huajun Chen
user
HuajunChen
67e4b0b850ca38886d7e78d6
Ningyu Zhang
admin_assigned
2025-03-27T10:21:07.281Z
620b3bbb0668e435407c8d0a
/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving
  Systems
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.
Knowledge Editing and ADS-Edit enhance Large Multimodal Models for Autonomous Driving Systems through targeted modifications and comprehensive real-world testing.
Large Multimodal Models Autonomous Driving Systems Knowledge Editing ADS-Edit multimodal knowledge editing real-world scenarios