分析自动驾驶视觉系统漏洞，揭示安全威胁与防护策略。
Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectors
- 构建自动驾驶视觉系统参考架构，明确关键组件
- 识别多类攻击向量，评估对机密性、完整性、可用性的威胁
- 为高阶自动驾驶系统提供安全设计指南
本文研究连接式自动驾驶车辆（CAV）视觉系统的鲁棒性，这对实现5级自动驾驶至关重要。安全可靠的导航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。我们分析了自动驾驶导航所需的关键传感器与视觉组件，提出了自动驾驶视觉系统（CAVVS）的参考架构，该架构为识别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的攻击向量，并严格评估其对机密性、完整性与可用性（CIA）的影响。本研究提供了对视觉系统攻击向量动态的全面理解，对于制定能维护CIA三原则的稳健安全措施具有重要意义。
原文摘要 · Abstract (English)
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.
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