Artificial Intelligence-Driven and Secure 5G-VANET Architectures for Future Transportation Systems - Saare, Murtaja Ali; Abdulhamed, Mohamed Abdulrahman; Al-Shareeda‬‏, ‪Mahmood A.; Almaiah, Mohammed Amin; Shehab, Rami (Universitas Muhammadiyah Yogyakarta, 2025)
The advent of 5G has opened a new era of intelligent, adaptive and secure VANETs that is envisaged to serve as the backbone network architecture for next generation of vehicular transportation systems. In this work, we present a connected 5G VANETs-to-Edge Computing systems with Artificial Intelligence (AI) infrastructure to improve system adaptability, anomaly detection, trust management, and real-time decisionmaking. Crucial enabling technologies like Software-Defined Networking (SDN). Mobile Edge Computing (MEC), and millimeterwave communication are investigated in detail. We examine key security threats such as identity forgery, data interception, and denial-of-service attacks, and assess the AI-enhanced defense measures such as intrusion detection systems and blockchainbased trust models. Applications, like autonomous platooning, and collaborative vehicle authentication provide additional examples of AI technologies’ added value in the context of vehicular communications and safety. The paper concludes by providing open issues and future directions, including quantum-resistant protocols, lightweight AI models and cognitive networking in the context AI-driven 5G-VANET ecosystems.