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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: 20 December 2024 | Viewed by 1330

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 (2 papers)

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Research

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 458
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
Viewed by 609
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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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Enhancing Differentiated Services in Heterogeneous V2X Networks
Authors: Chenn-Jung Huang, Han Chang, Li, Kai-Wen Hu, Yi-Hung Lien, and Hao-Wen Cheng
Affiliation: National Dong Hwa University
Abstract: In the foreseeable future, as electric vehicles become increasingly dominant in urban transportation, the exponential growth of vehicular communication is set to place significant pressure on the wireless transmission spectrum. This strain will be particularly pronounced when vehicle users engage in streaming services while on the move. The rapid expansion, driven by in-vehicle video streaming infotainment applications, underscores the need for precise bandwidth allocation tailored to the unique requirements of vehicular users. Notably, there has been a substantial increase in the production and consumption of omnidirectional or 360-degree videos, supported by recent technological advancements in networking and computing, as well as users' growing interest in enhancing their experiences. This gap becomes even more evident when considering the effective integration of emerging wireless communication technologies. To address this identified gap, our research introduces an innovative approach that leverages emerging 6G wireless communication technologies to provide wireless communication support for vehicular users with varying bandwidth requirements across a diverse range of services, including videotelephony, standard video, and 360-degree video. The ultimate goal is to ensure a seamless and gratifying streaming experience for vehicular users. Our simulation results substantiate that the presented algorithm can efficiently allocate bandwidth to vehicular users based on their video streaming requirements, ensuring a satisfactory user experience. Furthermore, the system optimally redistributes bandwidth from less congested base stations to areas with congestion, enhancing overall bandwidth utilization. In summary, our findings provide compelling evidence of the feasibility of this research in addressing the future video streaming needs of vehicular users to guarantee their satisfaction.

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