ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving
  Systems
Authors: name: Chenxi Wang, name: Jizhan Fang, name: Xiang Chen, name: Bozhong Tian, name: Ziwen Xu, name: Huajun Chen, name: Ningyu Zhang

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.