AI RESEARCH
Visual Adversarial Attack on Vision-Language Models for Autonomous Driving
arXiv CS.CV
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ArXi:2411.18275v2 Announce Type: replace Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities. However, these models remain highly vulnerable to adversarial attacks. While existing research has primarily focused on general VLM attacks, the development of attacks tailored to the safety-critical AD context has been largely overlooked. In this paper, we take the first step toward designing adversarial attacks specifically targeting VLMs in AD, exposing the substantial risks these attacks pose within this critical domain.