Convergent Safety-Security Risk Assessment for V2X-Enabled Connected and Autonomous Vehicles: A Comprehensive Survey and Research Roadmap
Abstract
Connected and Autonomous Vehicles (CAVs) with Vehicle-to-Everything (V2X) communication are transforming transportation systems, but their increasing connectivity and automation introduce critical challenges where safety and cybersecurity intersect. Traditional approaches treat safety analysis through Hazard Analysis and Risk Assessment (HARA) under ISO 26262 and cybersecurity assessment through Threat Analysis and Risk Assessment (TARA) under ISO/SAE 21434 as separate disciplines, yet cyberattacks on V2X-enabled CAVs can directly trigger safety-critical failures such as collision risks, system malfunctions, and compromised vehicle control. This survey presents a comprehensive analysis of convergent HARA-TARA methodologies specifically designed for V2X-enabled CAVs. The fundamentals of safety and security risk assessment are systematically reviewed; a structured taxonomy of challenges arising from their convergence is presented; and existing methodologies are classified into five categories, including model-based approaches, formal methods, data-driven techniques, ontology-based frameworks, and hybrid solutions. The analysis reveals critical gaps in current practices, particularly the inability of traditional methods to address machine learning uncertainties and adaptive cyber threats, while advanced verification techniques face practical deployment challenges. Major convergence frameworks are examined, and fundamental trade-offs between analytical rigor, practical applicability, and lifecycle coverage are identified. Systematic insights into the state of the art in integrated safety-security risk assessment are provided, and a comprehensive research roadmap is proposed to address near-term standardization needs, medium-term advances in compositional verification, and long-term challenges in certifying learning-enabled autonomous systems for safe and secure deployment at scale.