  As physical adversarial attacks become extensively applied in unearthing the
potential risk of security-critical scenarios, especially in dynamic scenarios,
their vulnerability to environmental variations has also been brought to light.
The non-robust nature of physical adversarial attack methods brings
less-than-stable performance consequently. Although methods such as EOT have
enhanced the robustness of traditional contact attacks like adversarial
patches, they fall short in practicality and concealment within dynamic
environments such as traffic scenarios. Meanwhile, non-contact laser attacks,
while offering enhanced adaptability, face constraints due to a limited
optimization space for their attributes, rendering EOT less effective. This
limitation underscores the necessity for developing a new strategy to augment
the robustness of such practices. To address these issues, this paper
introduces the Embodied Laser Attack (ELA), a novel framework that leverages
the embodied intelligence paradigm of Perception-Decision-Control to
dynamically tailor non-contact laser attacks. For the perception module, given
the challenge of simulating the victim's view by full-image transformation, ELA
has innovatively developed a local perspective transformation network, based on
the intrinsic prior knowledge of traffic scenes and enables effective and
efficient estimation. For the decision and control module, ELA trains an attack
agent with data-driven reinforcement learning instead of adopting
time-consuming heuristic algorithms, making it capable of instantaneously
determining a valid attack strategy with the perceived information by
well-designed rewards, which is then conducted by a controllable laser emitter.
Experimentally, we apply our framework to diverse traffic scenarios both in the
digital and physical world, verifying the effectiveness of our method under
dynamic successive scenes.
