EVAA—Exchange Vanishing Adversarial Attack on LiDAR Point Clouds in Autonomous Vehicles

In addition to red-green-blue (RGB) camera sensors, light detection and ranging (LiDAR) plays an important role in autonomous vehicles (AVs) to perceive their surroundings. Deep neural networks (DNNs) are able to achieve cutting-edge 3-D object detection and segmentation performance using LiDAR point clouds. LiDAR-enabled AVs provide human perception by segmenting LiDAR point clouds into meaningful regions and providing semantic context to the AV user. However, the generation of point clouds to provide semantic segmentation in AVs is not reliable and secure, which may result in traffic accidents. We propose a novel adversarial attack against LiDAR point clouds in AVs in this article. We devised an exchange vanishing adversarial attack (EVAA) to deceive LiDAR point clouds by introducing targeted noise on specific objects (e.g., vehicles and driveways). In two autonomous driving datasets with 3-D object annotations, NuScenes and PandaSet, we evaluate the performance of our proposed attack framework. We achieve an attack success rate (ASR) of
$\approx 63$
% and ASR of
$\approx 29$
% on both NuScenes and PandaSet datasets, respectively.