自动驾驶是未来全球科技发展重点关注领域之一。安全性、可靠性是自动驾驶技术大规模商业化应用的最重要前提。自动驾驶系统是一个大规模复杂系统，该系统包含了感知、规划和控制等多个子模块，复杂系统结构在很大程度上影响了自动驾驶技术的可靠性。目前，针对自动驾驶系统的可靠性研究尚不充分，因此有必要对自动驾驶系统的可靠性进行更深一步的测试研究。 本文首先针对自动驾驶系统中感知模块中的深度学习模型展开了可靠性测试研究。感知模块识别周围环境主要依赖于深度学习模型，以往的研究已经证实深度学习模型由于自身缺陷，易被攻击和欺骗，进而导致错误识别周围环境。本研究对感知模块的可靠性提出了一种新的对抗测试工具，即蒙特卡洛快速梯度攻击算法，该算法不需要模型的任何先验知识。该算法首先利用蒙特卡洛采样法估计深度学习模型的梯度，之后基于反向梯度上升方式攻击深度学习模型。另外，本研究还给出了该对抗测试工具梯度估计无偏性的证明。相对于传统梯度对抗测试方法，本研究将对抗测试的时间复杂度从$ O(n) $优化至$ O(1) $。本研究提出的蒙特卡洛快速梯度攻击算法可以作为自动驾驶系统的可靠性评估工具。 本文还对自动驾驶系统软硬件在环可靠性测试问题展开了研究，并设计了一个三阶段测试框架。该框架第一阶段，搭建硬件在环测试实验台，该平台包含自动驾驶系统组件、虚拟场景组件、控制器局域网组件和环境应力控制组件。第二阶段，在实验台基础上设计正交实验，本研究设计了温度、湿度和振动的正交实验并选取了能够反映软硬件综合性能的测试指标。第三阶段，建立函数型数据分析，本研究首先建立了函数型方差分析，以及函数型混合回归模型用于研究性能指标与环境压力之间的关系。另外，根据分析结果，本研究还给出了物理机理解释以及自动驾驶系统性能改进方法。本文设计的三阶段框架是可以作为自动驾驶可靠性测试研究的基础。

Autonomous driving is one of the key areas of global technology development in the future. Safety and reliability are the most important prerequisites for the large-scale commercial application of autonomous driving technology. The autonomous driving system is a large-scale complex system, which includes multiple submodules such as perception, planning, and control modules. Therefore, the reliability of autonomous driving technology may be affected by the complex structure of the system. At present, the research on the reliability of autonomous driving systems often focuses on a single substructure, and there is still a gap in the research on its comprehensive software and hardware joint testing. Therefore, it is necessary to conduct further studies on the reliability of the autonomous driving system.In this paper, the reliability testing research is first carried out for the deep learning model in the perception module of the autonomous driving system. The deep learning model is a vital part of the perception module, which is capable to recognize the surrounding environment. However, previous studies have confirmed that the deep learning model is vulnerable to adversarial attacks due to its structure knowledge, which may lead to misunderstanding of the surrounding environment. This paper proposes a novel adversarial attack method for the reliability testing of perception module, that is, the Monte Carlo Fast Gradient Sign Method (MC-FGSM). This method can attack the model without any prior knowledge of the victim model. This method first uses Monte Carlo sampling to estimate the gradient of the deep learning model, and then the gradient ascent method can be deployed to attack the victim model. Moreover, strict mathematical proof has shown the unbiasedness of the gradient estimation method. Compared with the traditional gradient attack method, this paper optimizes the time complexity of the adversarial test from $O (n) $ to $O (1)$. The MC-FGSM algorithm proposed in this paper can work as the reliability evaluation tool of the autonomous driving system.This article also conducts research on the issues of the software-and-hardware-in-the-loop reliability testing of autonomous driving system, and designs a three-stage testing framework. In the first stage, a software-and-hardware-in-the-loop testbed is constructed, which comprises four components, i.e., the vehicle simulation component, the virtual scenery component, the controller area network component, and the environmental stressors control component. In the second stage, orthogonal experiments are designed based on the testbed. The orthogonal experiments for temperature, humidity, and vibration is deployed, meanwhile one performance indicator that could reflect the status of both software and hardware is also selected. In the third stage, functional data analysis was conducted. This stage deploys the functional data analysis of variance and functional mixed regression model to investigate the relationship between performance indicators and the environmental stressors. Additionally, based on the analysis results, the physical mechanism explanation and the methods for improving the performance of the autonomous driving system are also provided. The three-stage framework designed in this article can serve as a basis for research on autonomous driving reliability testing.