Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectors

TL;DR

This paper examines the security vulnerabilities of vision systems in connected and autonomous vehicles, identifying attack vectors and proposing a framework to enhance their robustness for safe autonomous driving.

Contribution

It introduces a reference architecture for CAV vision systems and analyzes potential attack surfaces and vectors to improve security measures.

Findings

Identified key attack vectors on CAV vision systems

Evaluated attack implications on confidentiality, integrity, and availability

Provided insights for developing more secure autonomous vehicle vision systems

Abstract

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.