Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke. In this paper, we introduce a novel adversarial perspective by proposing an attack framework that utilizes water mist and smoke to simulate environmental interference. Specifically, we propose a point cloud sequence generation method using a motion and content decomposition generative adversarial network named PCS-GAN to simulate the distribution of random objects. Furthermore, leveraging the simulated LiDAR scanning characteristics implemented with Range Image, we examine the effects of introducing random object perturbations at various positions on the target vehicle. Extensive experiments demonstrate that adversarial perturbations based on random objects effectively deceive vehicle detection and reduce the recognition rate of 3D object detection models.

翻译：自动驾驶车辆依赖激光雷达传感器在驾驶场景中进行环境感知与决策。然而，在复杂环境中确保自动驾驶车辆的安全性与可靠性仍是一项紧迫挑战。为解决该问题，我们引入了一个真实世界数据集（ROLiD），其中包含两种随机物体——水雾与烟雾——的激光雷达扫描点云。本文提出一种新颖的对抗视角，通过构建一种利用水雾与烟雾模拟环境干扰的攻击框架。具体而言，我们提出一种基于运动与内容解耦生成对抗网络（命名为PCS-GAN）的点云序列生成方法，以模拟随机物体的分布特性。进一步地，结合通过距离图像实现的模拟激光雷达扫描特征，我们研究了在目标车辆不同位置引入随机物体扰动所产生的影响。大量实验表明，基于随机物体的对抗扰动能有效欺骗车辆检测，并降低三维目标检测模型的识别率。