论文标题
使用3D阴影检测对象隐藏对自动驾驶汽车知觉的攻击
Using 3D Shadows to Detect Object Hiding Attacks on Autonomous Vehicle Perception
论文作者
论文摘要
自动驾驶汽车（AV）主要依赖于LiDAR传感器，该传感器能够对周围环境进行空间感知并有助于做出决定。最近的作品表明了旨在将物体隐藏到AV感知的攻击，这可能会导致严重的后果。 3D阴影是在3D点云中没有测量的区域，这是由场景中对象的遮挡产生的。提出3D阴影是用于检测欺骗或假物体的物理不变的物理不变。在这项工作中，我们利用3D阴影来定位从对象探测器中隐藏的障碍物。我们通过寻找空隙区域并找到引起这些阴影的障碍来实现这一目标。我们提出的方法可用于检测一个对象隐藏的对象，因为这些对象被隐藏在3D对象检测器中，但仍会在3D点云中诱导阴影伪影，我们将其用于障碍物检测。我们表明，使用3D阴影进行障碍物检测可以实现高精度，将阴影与它们的对象匹配，并提供障碍物与自我车辆的距离的精确预测。
Autonomous Vehicles (AVs) are mostly reliant on LiDAR sensors which enable spatial perception of their surroundings and help make driving decisions. Recent works demonstrated attacks that aim to hide objects from AV perception, which can result in severe consequences. 3D shadows, are regions void of measurements in 3D point clouds which arise from occlusions of objects in a scene. 3D shadows were proposed as a physical invariant valuable for detecting spoofed or fake objects. In this work, we leverage 3D shadows to locate obstacles that are hidden from object detectors. We achieve this by searching for void regions and locating the obstacles that cause these shadows. Our proposed methodology can be used to detect an object that has been hidden by an adversary as these objects, while hidden from 3D object detectors, still induce shadow artifacts in 3D point clouds, which we use for obstacle detection. We show that using 3D shadows for obstacle detection can achieve high accuracy in matching shadows to their object and provide precise prediction of an obstacle's distance from the ego-vehicle.
