Ensuring autonomous vehicle (AV) security remains a critical concern. An area of paramount importance is the study of physical-world adversarial examples (AEs) aimed at exploiting vulnerabilities in perception systems. However, most of the prevailing research on AEs has neglected considerations of stealthiness and legality, resulting in scenarios where human drivers would promptly intervene or attackers would be swiftly detected and punished. These limitations hinder the applicability of such examples in real-life settings. In this paper, we introduce a novel approach to generate AEs using what we term negative shadows: deceptive patterns of light on the road created by strategically blocking sunlight, which then cast artificial lane-like patterns. These shadows are inconspicuous to a driver while deceiving AV perception systems, particularly those reliant on lane detection algorithms. By prioritizing the stealthy nature of attacks to minimize driver interventions and ensuring their legality from an attacker's standpoint, a more plausible range of scenarios is established. In multiple scenarios, including at low speeds, our method shows a high safety violation rate. Using a 20-meter negative shadow, it can direct a vehicle off-road with a 100% violation rate at speeds over 10 mph. Other attack scenarios, such as causing collisions, can be performed with at least 30 meters of negative shadow, achieving a 60-100% success rate. The attack also maintains an average stealthiness of 83.6% as measured through a human subject experiment, ensuring its efficacy in covert settings.

翻译：确保自动驾驶车辆（AV）安全仍是一个关键问题。物理世界对抗样本（AEs）的研究至关重要，其旨在利用感知系统的漏洞。然而，现有大多数关于对抗样本的研究忽视了隐蔽性与合法性的考量，导致人类驾驶员会立即介入或攻击者迅速被检测并受到惩罚的场景。这些限制阻碍了此类样本在现实场景中的适用性。本文提出一种利用负阴影生成对抗样本的新方法：通过策略性遮挡阳光在路面上形成的光学欺骗模式，进而投射出类似车道的虚假图案。这些阴影对驾驶员而言难以察觉，却能欺骗依赖车道检测算法的自动驾驶感知系统。通过优先考虑攻击的隐蔽性以最小化驾驶员干预，并确保从攻击者角度的合法性，我们建立了更合理的场景范围。在包括低速行驶的多种场景中，我们的方法显示出较高的安全违规率。使用20米长的负阴影，可在车速超过10英里/小时时以100%的违规率引导车辆偏离道路。其他攻击场景（如引发碰撞）可通过至少30米的负阴影实现，成功率可达60-100%。根据人类受试者实验测量，该攻击平均隐蔽性达83.6%，确保了其在隐蔽场景中的有效性。