  Deep learning-based lane detection (LD) plays a critical role in autonomous
driving systems, such as adaptive cruise control. However, it is vulnerable to
backdoor attacks. Existing backdoor attack methods on LD exhibit limited
effectiveness in dynamic real-world scenarios, primarily because they fail to
consider dynamic scene factors, including changes in driving perspectives
(e.g., viewpoint transformations) and environmental conditions (e.g., weather
or lighting changes). To tackle this issue, this paper introduces BadLANE, a
dynamic scene adaptation backdoor attack for LD designed to withstand changes
in real-world dynamic scene factors. To address the challenges posed by
changing driving perspectives, we propose an amorphous trigger pattern composed
of shapeless pixels. This trigger design allows the backdoor to be activated by
various forms or shapes of mud spots or pollution on the road or lens, enabling
adaptation to changes in vehicle observation viewpoints during driving. To
mitigate the effects of environmental changes, we design a meta-learning
framework to train meta-generators tailored to different environmental
conditions. These generators produce meta-triggers that incorporate diverse
environmental information, such as weather or lighting conditions, as the
initialization of the trigger patterns for backdoor implantation, thus enabling
adaptation to dynamic environments. Extensive experiments on various commonly
used LD models in both digital and physical domains validate the effectiveness
of our attacks, outperforming other baselines significantly (+25.15% on average
in Attack Success Rate). Our codes will be available upon paper publication.
