Intelligent Technologies for Vehicular Networks, 2nd Edition

Dear Colleagues,

In recent years, the realm of Intelligent Transport Systems (ITS) has undergone a significant surge, driven by a profound focus on harnessing the potential of the Internet of Vehicles (IoV). This surge encompasses efforts to address security and privacy concerns within vehicular networks, exploit vehicular clouds to enhance neighboring vehicle capabilities, and pioneer novel routing protocols to optimize communications amidst the challenges of high mobility and intermittent connections. This burgeoning domain has witnessed the emergence of intelligent technologies that underpin the development of sophisticated vehicular systems, facilitating seamless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. From autonomous vehicles to collaborative advanced driver assistance systems (co-ADAS), these technologies enable groundbreaking functionalities such as real-time video streaming for enhanced road visibility during overtaking maneuvers and the establishment of robust vehicle surveillance systems.

The primary aim of this Special Issue is to present scholarly contributions that delve into unresolved challenges within next-generation vehicular networks while also providing insightful surveys to discern emerging trends and identify nascent research frontiers. Encompassing a diverse array of topics, submissions are encouraged to explore the manifold possibilities afforded by the Internet of Things (IoT) in shaping protocols, applications, and services tailored to IoV-connected devices. Furthermore, special emphasis is placed on the integration of machine learning and deep learning algorithms due to their pivotal role in enabling intelligent management across various facets of vehicular systems.

Deep learning models offer immense potential to revolutionize vehicular networks by enhancing traffic management, road safety, V2X communications, and more. They can predict congestion to optimize traffic flow, detect objects for improved road safety, and ensure reliable V2X communication. Additionally, deep learning powers autonomous driving systems, facilitates predictive maintenance, analyzes driver behavior, and provides real-time environmental data for adaptive driving. Approaches that explore the possibilities of deep learning to make transportation systems safer, more efficient, and smarter overall are highly encouraged.

Prof. Dr. Yolanda Blanco Fernández
Guest Editor

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