This article investigates the robustness of vision systems in Connected and Autonomous Vehicles (CAVs), which is critical for developing Level-5 autonomous driving capabilities. Safe and reliable CAV navigation undeniably depends on robust vision systems that enable accurate detection of objects, lane markings, and traffic signage. We analyze the key sensors and vision components essential for CAV navigation to derive a reference architecture for CAV vision system (CAVVS). This reference architecture provides a basis for identifying potential attack surfaces of CAVVS. Subsequently, we elaborate on identified attack vectors targeting each attack surface, rigorously evaluating their implications for confidentiality, integrity, and availability (CIA). Our study provides a comprehensive understanding of attack vector dynamics in vision systems, which is crucial for formulating robust security measures that can uphold the principles of the CIA triad.

翻译：本文研究了网联自动驾驶汽车（CAV）中视觉系统的鲁棒性，这对于实现L5级自动驾驶能力至关重要。安全可靠的CAV导航无疑依赖于能够准确检测物体、车道线和交通标志的鲁棒视觉系统。我们分析了CAV导航所需的关键传感器与视觉组件，从而推导出CAV视觉系统（CAVVS）的参考架构。该参考架构为识别CAVVS的潜在攻击面提供了基础。随后，我们详细阐述了针对每个攻击面所识别的攻击向量，并严格评估了它们对机密性、完整性和可用性（CIA）原则的影响。本研究提供了对视觉系统中攻击向量动态的全面理解，这对于制定能够维护CIA三原则的鲁棒安全措施至关重要。