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Intelligent Technologies for Vehicular Networks, 2nd Edition
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Intelligent Technologies for Vehicular Networks, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 5540

Special Issue Editor

Prof. Dr. Yolanda Blanco Fernández

E-Mail Website
Guest Editor
atlanTTic research Center for Telecommunication Technologies, University of Vigo, 36310 Vigo, Spain
Interests: semantic reasoning in personalization applications; machine learning techniques; deep learning models for natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • vehicular networks
  • machine learning
  • vehicle-to-everything (V2X)
  • resource allocation
  • intelligent vehicular systems
  • deep learning
  • recurrent neural networks (RNNs)
  • convolutional neural networks (CNNs)
  • IoT
  • IoV
  • networking
  • cloud-based vehicular technologies

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Related Special Issue

Published Papers (6 papers)

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Research

19 pages, 1237 KiB  
Article
A Seamless Authentication Scheme for Edge-Assisted Internet of Vehicles Environments Using Chaotic Maps
by Seunghwan Son, DeokKyu Kwon and Youngho Park
Electronics 2025, 14(4), 672; https://doi.org/10.3390/electronics14040672 - 9 Feb 2025
Viewed by 486
Abstract
Internet of Vehicles (IoV) is a concept that combines IoT and vehicular ad hoc networks. In IoV environments, vehicles constantly move and communicate with other roadside units (edge servers). Due to the vehicles’ insufficient computing power, repetitive authentication procedures can be burdensome for [...] Read more.
Internet of Vehicles (IoV) is a concept that combines IoT and vehicular ad hoc networks. In IoV environments, vehicles constantly move and communicate with other roadside units (edge servers). Due to the vehicles’ insufficient computing power, repetitive authentication procedures can be burdensome for automobiles. In recent years, numerous authentication protocols for IoV environments have been proposed. However, there is no study that considers both re-authentication and handover authentication situations, which are essential for seamless communication in vehicular networks. In this study, we propose a chaotic map-based seamless authentication scheme for edge-assisted IoV environments. We propose authentication protocols for initial, handover, and re-authentication situations and analyze the security of our scheme using informal methods, the real-or-random (RoR) model, and the Scyther tool. We also compare the proposed scheme with existing schemes and show that our scheme has superior performance and provides more security features. To our knowledge, This paper is the first attempt to design an authentication scheme considering both handover and re-authentication in the IoV environment. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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22 pages, 4054 KiB  
Article
Collision Avoidance in Autonomous Vehicles Using the Control Lyapunov Function–Control Barrier Function–Quadratic Programming Approach with Deep Reinforcement Learning Decision-Making
by Haochong Chen, Fengrui Zhang and Bilin Aksun-Guvenc
Electronics 2025, 14(3), 557; https://doi.org/10.3390/electronics14030557 - 30 Jan 2025
Viewed by 917
Abstract
Collision avoidance and path planning are critical topics in autonomous vehicle development. This paper presents the progressive development of an optimization-based controller for autonomous vehicles using the Control Lyapunov Function–Control Barrier Function–Quadratic Programming (CLF-CBF-QP) approach. This framework enables a vehicle to navigate to [...] Read more.
Collision avoidance and path planning are critical topics in autonomous vehicle development. This paper presents the progressive development of an optimization-based controller for autonomous vehicles using the Control Lyapunov Function–Control Barrier Function–Quadratic Programming (CLF-CBF-QP) approach. This framework enables a vehicle to navigate to its destination while avoiding obstacles. A unicycle model is utilized to incorporate vehicle dynamics. A series of simulations were conducted, starting with basic model-in-the-loop (MIL) non-real-time simulations, followed by real-time simulations. Multiple scenarios with different controller configurations and obstacle setups were tested, demonstrating the effectiveness of the proposed controllers in avoiding collisions. Real-time simulations in Simulink were used to demonstrate that the proposed controller could compute control actions for each state within a very short timestep, highlighting its computational efficiency. This efficiency underscores the potential for deploying the controller in real-world vehicle autonomous driving systems. Furthermore, we explored the feasibility of a hierarchical control framework comprising deep reinforcement learning (DRL), specifically a Deep Q-Network (DQN)-based high-level controller and a CLF-CBF-QP-based low-level controller. Simulation results show that the vehicle could effectively respond to obstacles and generate a successful trajectory towards its goal. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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19 pages, 4022 KiB  
Article
Framework for Analyzing Spatial Interference in Vehicle-to-Vehicle Communication Networks with Positional Errors
by Nivetha Kanthasamy and Alexander Wyglinski
Electronics 2025, 14(3), 510; https://doi.org/10.3390/electronics14030510 - 26 Jan 2025
Viewed by 855
Abstract
This paper introduces a novel framework for evaluating vehicle-to-vehicle (V2V) communication systems, employing beamforming and null steering techniques. Addressing challenges such as electromagnetic interference from other vehicles within the network as well as diverse road conditions, the framework defines the simulation environment for [...] Read more.
This paper introduces a novel framework for evaluating vehicle-to-vehicle (V2V) communication systems, employing beamforming and null steering techniques. Addressing challenges such as electromagnetic interference from other vehicles within the network as well as diverse road conditions, the framework defines the simulation environment for V2V networks across different traffic scenarios to assess system reliability. The analytical components of the proposed framework are structured as follows: a comprehensive framework is developed, serving as the basis for implementing a simulator that leverages advanced spatial signal processing algorithms to evaluate V2V networks across specific scenarios, accounting for the effects of positional errors. The framework integrates multiple blocks, including road modeling, system modeling, and performance evaluation, providing adaptability for different algorithms or configurations. Positional inaccuracies are examined, highlighting their effects on system performance, particularly in scenarios where null steering accuracy is imperfect, thus underscoring the need for enhanced interference management strategies. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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25 pages, 11970 KiB  
Article
General Obstacle Avoidance Capability Assessment for Autonomous Vehicles
by Evan Lowe and Levent Guvenc
Electronics 2024, 13(24), 4901; https://doi.org/10.3390/electronics13244901 - 12 Dec 2024
Viewed by 792
Abstract
As autonomous vehicle (AV) capabilities expand, it is important to ensure their safety during testing and deployment for public usage. While several testing regulations have been proposed in research, US federal, and even global guidelines for low-speed vehicles in metropolitan settings, regulations for [...] Read more.
As autonomous vehicle (AV) capabilities expand, it is important to ensure their safety during testing and deployment for public usage. While several testing regulations have been proposed in research, US federal, and even global guidelines for low-speed vehicles in metropolitan settings, regulations for high-speed travel are mainly vacant—this is especially true for regulations relating to AV emergency obstacle avoidance maneuvers (EOAMs). Research in this manuscript introduces a general obstacle avoidance capability assessment (GOACA) for AVs traveling at highway speeds. This GOACA includes test modes including car and bicycle active road objects (AROs) in rural and urban highway settings. These tests were novel in their definitions, methodologies, and execution, especially in the context of AVs driving at highway speeds—critically, this research proposes a test evaluation protocol such that it could serve as a foundation for an official regulation in the future. These GOACA tests included adjacent traffic vehicles which have not been utilized in the prior literature when considering EOAMs within a wholistic AV system context. While the vehicle type will cause simulation results to var, in general, vehicle-to-vehicle (V2V) communication is recommended for usage with AVs at highway speeds for critical safety. This is especially true when considering oncoming traffic and low surface μ conditions. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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17 pages, 1418 KiB  
Article
A Genetic Optimized Federated Learning Approach for Joint Consideration of End-to-End Delay and Data Privacy in Vehicular Networks
by Müge Erel-Özçevik, Akın Özçift, Yusuf Özçevik and Fatih Yücalar
Electronics 2024, 13(21), 4261; https://doi.org/10.3390/electronics13214261 - 30 Oct 2024
Viewed by 1002
Abstract
In 5G vehicular networks, two key challenges have become apparent, including end-to-end delay minimization and data privacy. Learning-based approaches have been used to alleviate these, either by predicting delay or protecting privacy. Traditional approaches train machine learning models on local devices or cloud [...] Read more.
In 5G vehicular networks, two key challenges have become apparent, including end-to-end delay minimization and data privacy. Learning-based approaches have been used to alleviate these, either by predicting delay or protecting privacy. Traditional approaches train machine learning models on local devices or cloud servers, each with their own trade-offs. While pure-federated learning protects privacy, it sacrifices delay prediction performance. In contrast, centralized training improves delay prediction but violates privacy. Existing studies in the literature overlook the effect of training location on delay prediction and data privacy. To address both issues, we propose a novel genetic algorithm optimized federated learning (GAoFL) approach in which end-to-end delay prediction and data privacy are jointly considered to obtain an optimal solution. For this purpose, we analytically define a novel end-to-end delay formula and data privacy metrics. Accordingly, a novel fitness function is formulated to optimize both the location of training model and data privacy. In conclusion, according to the evaluation results, it can be advocated that the outcomes of the study highlight that training location significantly affects privacy and performance. Moreover, it can be claimed that the proposed GAoFL improves data privacy compared to centralized learning while achieving better delay prediction than other federated methods, offering a valuable solution for 5G vehicular computing. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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20 pages, 2809 KiB  
Article
Stability of Local Trajectory Planning for Level-2+ Semi-Autonomous Driving without Absolute Localization
by Sheng Zhu, Jiawei Wang, Yu Yang and Bilin Aksun-Guvenc
Electronics 2024, 13(19), 3808; https://doi.org/10.3390/electronics13193808 - 26 Sep 2024
Cited by 1 | Viewed by 1059
Abstract
Autonomous driving has long grappled with the need for precise absolute localization, making full autonomy elusive and raising the capital entry barriers for startups. This study delves into the feasibility of local trajectory planning for Level-2+ (L2+) semi-autonomous vehicles without the dependence on [...] Read more.
Autonomous driving has long grappled with the need for precise absolute localization, making full autonomy elusive and raising the capital entry barriers for startups. This study delves into the feasibility of local trajectory planning for Level-2+ (L2+) semi-autonomous vehicles without the dependence on accurate absolute localization. Instead, emphasis is placed on estimating the pose change between consecutive planning timesteps from motion sensors and on integrating the relative locations of traffic objects into the local planning problem within the ego vehicle’s local coordinate system, thereby eliminating the need for absolute localization. Without the availability of absolute localization for correction, the measurement errors of speed and yaw rate greatly affect the estimation accuracy of the relative pose change between timesteps. This paper proved that the stability of the continuous planning problem under such motion sensor errors can be guaranteed at certain defined conditions. This was achieved by formulating it as a Lyapunov-stability analysis problem. Moreover, a simulation pipeline was developed to further validate the proposed local planning method, which features adjustable driving environment with multiple lanes and dynamic traffic objects to replicate real-world conditions. Simulations were conducted at two traffic scenes with different sensor error settings for speed and yaw rate measurements. The results substantiate the proposed framework’s functionality even under relatively inferior sensor errors distributions, i.e., speed error verr∼N(−0.1,0.1) m/s and yaw rate error θ˙err∼N(0.57,1.72) deg/s. Experiments were also conducted to evaluate the stability limits of the planned results under abnormally larger motion sensor errors. The results provide a good match to the previous theoretical analysis. Our findings suggested that precise absolute localization may not be the sole path to achieving reliable trajectory planning, eliminating the necessity for high-accuracy dual-antenna Global Positioning System (GPS) as well as the pre-built high-fidelity (HD) maps for map-based localization. Full article
(This article belongs to the Special Issue Intelligent Technologies for Vehicular Networks, 2nd Edition)
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