Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectors
It addresses security challenges for vision systems in autonomous vehicles, which is critical for safe navigation, but is incremental as it focuses on analysis and identification rather than novel solutions.
This paper investigates the robustness of vision systems in Connected and Autonomous Vehicles (CAVs) by analyzing sensors and components to derive a reference architecture, identifying attack vectors, and evaluating their implications for confidentiality, integrity, and availability to inform security measures.
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.