For autonomous driving, an essential task is to detect surrounding objects accurately. To this end, most existing systems use optical devices, including cameras and light detection and ranging (LiDAR) sensors, to collect environment data in real time. In recent years, many researchers have developed advanced machine learning models to detect surrounding objects. Nevertheless, the aforementioned optical devices are vulnerable to optical signal attacks, which could compromise the accuracy of object detection. To address this critical issue, we propose a framework to detect and identify sensors that are under attack. Specifically, we first develop a new technique to detect attacks on a system that consists of three sensors. Our main idea is to: 1) use data from three sensors to obtain two versions of depth maps (i.e., disparity) and 2) detect attacks by analyzing the distribution of disparity errors. In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method. Based on the detection scheme, we further develop an identification model that is capable of identifying up to n-2 attacked sensors in a system with one LiDAR and n cameras. We prove the correctness of our identification scheme and conduct experiments to show the accuracy of our identification method. Finally, we investigate the overall sensitivity of our framework.
翻译:对于自主驾驶而言,一项基本任务是准确探测周围物体。为此,大多数现有系统都使用光学装置,包括照相机和光探测及测距传感器,实时收集环境数据。近年来,许多研究人员开发了先进的机器学习模型,以探测周围物体。然而,上述光学装置容易受到光学信号攻击,这可能会损害物体探测的准确性。为了解决这一关键问题,我们提出了一个探测和识别正在受到攻击的传感器的框架。具体地说,我们首先开发了一种探测由三个传感器组成的系统的攻击的新技术。我们的主要想法是:1)利用三个传感器的数据,通过分析差异误差的分布来获取两个版本的深度地图(即差异)和2),以探测攻击。在我们的研究中,我们使用真实的数据集和最先进的机器学习模型来评估攻击探测系统的准确性。我们根据探测方法,我们进一步开发了一种识别模型,能够用一个激光雷达和n摄像头来识别被攻击的N-2传感器。我们最后证明了我们的识别方法的准确性。我们用一个激光雷达和n摄像头来进行我们的精确性测试。