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.

中文翻译：

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检测和识别对自动驾驶系统的光信号攻击


对于自动驾驶来说，一个重要的任务是准确地检测周围的物体。为此，大多数现有系统使用光学设备，包括摄像头和光探测与测距（LiDAR）传感器来实时收集环境数据。近年来，许多研究人员开发了先进的机器学习模型来检测周围的物体。然而，上述光学设备容易受到光信号攻击，这可能会损害物体检测的准确性。为了解决这个关键问题，我们提出了一个框架来检测和识别受到攻击的传感器。具体来说，我们首先开发一种新技术来检测对由三个传感器组成的系统的攻击。我们的主要想法是：1）使用来自三个传感器的数据获得两个版本的深度图（即视差），2）通过分析视差误差的分布来检测攻击。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我们的攻击检测方案，结果证实了我们的检测方法的有效性。基于检测方案，我们进一步开发了一种识别模型，能够在具有一个 LiDAR 和 n 个摄像头的系统中识别最多 n-2 个受攻击的传感器。我们证明了我们的识别方案的正确性，并进行了实验来证明我们的识别方法的准确性。最后，我们研究框架的整体敏感性。