Autonomous Vehicles (AVs) increasingly use LiDAR-based object detection systems to perceive other vehicles and pedestrians on the road. While existing attacks on LiDAR-based autonomous driving architectures focus on lowering the confidence score of AV object detection models to induce obstacle misdetection, our research discovers how to leverage laser-based spoofing techniques to selectively remove the LiDAR point cloud data of genuine obstacles at the sensor level before being used as input to the AV perception. The ablation of this critical LiDAR information causes autonomous driving obstacle detectors to fail to identify and locate obstacles and, consequently, induces AVs to make dangerous automatic driving decisions. In this paper, we present a method invisible to the human eye that hides objects and deceives autonomous vehicles' obstacle detectors by exploiting inherent automatic transformation and filtering processes of LiDAR sensor data integrated with autonomous driving frameworks. We call such attacks Physical Removal Attacks (PRA), and we demonstrate their effectiveness against three popular AV obstacle detectors (Apollo, Autoware, PointPillars), and we achieve 45{\deg} attack capability. We evaluate the attack impact on three fusion models (Frustum-ConvNet, AVOD, and Integrated-Semantic Level Fusion) and the consequences on the driving decision using LGSVL, an industry-grade simulator. In our moving vehicle scenarios, we achieve a 92.7% success rate removing 90\% of a target obstacle's cloud points. Finally, we demonstrate the attack's success against two popular defenses against spoofing and object hiding attacks and discuss two enhanced defense strategies to mitigate our attack.

翻译：自动机动车辆(AV)越来越多地使用以LIDAR为基础的天体探测系统来感知路上的其他车辆和行人。虽然目前对LIDAR的自主驾驶结构的攻击侧重于降低AV物体探测模型的可信度分,以诱导发现障碍,但我们的研究发现,如何利用激光测谎技术来利用激光测谎技术有选择地删除LIDAR点云数据,从而在传感器一级有选择地删除LIDAR点云数据,然后才用作对AV感知的输入。这一关键的LIDAR信息崩溃导致自动驱动障碍探测器无法识别和定位障碍,从而诱使AVAR的自动驾驶结构作出危险的自动驾驶决定。在本文中,我们展示了隐藏物体的人类眼睛看不见的方法,通过利用与自主驱动框架相结合的LDAR传感器数据固有的自动转换和过滤程序,我们称之为“物理清除成功攻击”,我们用三种流行的AVAV级障碍探测器(Apolo、Autware、PpointPillars)来对抗障碍探测器,我们用45 目标值自动驾驶自动驾驶决定进行危险的自动驾驶决定。我们用SVDLLADLAVAVAVLA级攻击的移动能力,我们用AVLAVLLAVA级的升级来显示三级攻击速度和冲变压。我们用AVLRRLAVAVAVAV级攻击速度来评估了一种攻击速度。我们CLAV级攻击速度,我们变压。我们LLAVLLLLLLLLLAV级攻击速度,我们变压。我们变压。我们变压了二级的变压。我们变压。我们变压的变压。