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Vehicles
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Intelligent Connected Vehicles
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Intelligent Connected Vehicles

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1527

Special Issue Editors

Dr. Shih-lin Lin

E-Mail Website
Guest Editor
Graduate Institute of Vehicle Engineering, National Changhua University of Education, Changhua 50007, Taiwan
Interests: intelligent connected vehicles; artificial intelligence; data analysis; machine vision; deep learning
Dr. Wenbin Wan

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of New Mexico, MSC01 1150, Albuquerque, NM 87131, USA
Interests: cyber-physical systems; autonomous vehicles; resilient estimation; safe control methods; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue focuses on highlighting the progress and technological innovations in connected vehicles. With the rise of the Internet of Vehicles (IoV) and autonomous driving technologies, smart connected vehicles are becoming essential for transportation systems. The increasing demand for transportation solutions is driving the exploration and innovation of technologies in this field. This Special Issue aims to explore how vehicle intelligence merges with internet technologies, covering driving systems, in-vehicle networks, vehicular communication technologies, big data analysis, and applications of intelligence.

Research on vehicles involves integrating various technological areas. There is a growing need to develop tools that can effectively handle these systems while providing reliable analyses. These analyses not only improve our understanding of the processes involved but also greatly contribute to designing processes that accelerate progress in vehicle technologies.

In this research field, experiments are crucial. When paired with testing, they can accelerate the discovery of insights and advancements.

This Special Issue invites research contributions on interconnected cars, specifically how they interact with vehicles, infrastructures, and communication systems. We encourage practical or computational investigations (or a mix of these methods).

Dr. Shih-lin Lin
Dr. Wenbin Wan
Guest Editors

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. Vehicles is an international peer-reviewed open access quarterly 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 1600 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

  • intelligent connected vehicles
  • autonomous driving
  • Internet of Vehicles (IoV)
  • vehicular communication technologies
  • big data analytics
  • artificial intelligence
  • In-Vehicle networks
  • vehicle-to-vehicle (V2V) communication
  • vehicle-to-infrastructure (V2I) communication
  • smart transportation systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

27 pages, 2772 KiB  
Article
Game-Theoretic Cooperative Task Allocation for Multiple-Mobile-Robot Systems
by Lixiang Liu and Peng Li
Vehicles 2025, 7(2), 35; https://doi.org/10.3390/vehicles7020035 - 19 Apr 2025
Viewed by 155
Abstract
This study investigates the task allocation problem for multiple mobile robots in complex real-world scenarios. To address this challenge, a distributed game-theoretic approach is proposed to enable collaborative decision-making. First, the task allocation problem for multiple mobile robots is formulated to optimize the [...] Read more.
This study investigates the task allocation problem for multiple mobile robots in complex real-world scenarios. To address this challenge, a distributed game-theoretic approach is proposed to enable collaborative decision-making. First, the task allocation problem for multiple mobile robots is formulated to optimize the resource utilization. The formulation also takes into account comprehensive constraints related to robot positioning and task timing. Second, a game model is established for the proposed problem, which is proved to be an exact potential game. Furthermore, we introduce a novel utility function for the tasks to maximize the resource utilization. Based on this formulation, we develop a game-theoretic coalition formation algorithm to seek the Nash equilibrium. Finally, the algorithm is evaluated via simulation experiments. Another six algorithms are used for comparative studies. When the problem scale is small, the proposed algorithm can achieve solution quality comparable to that of the benchmark algorithms. In contrast, under larger and more complex problem instances, the proposed algorithm can achieve up to a 50% performance improvement over the benchmarks. This further confirms the effectiveness and superiority of the proposed method. In addition, we evaluate the solution quality and response time of the algorithm, as well as its sensitivity to initial conditions. Finally, the proposed algorithm is applied to a post-disaster rescue scenario, where the task allocation results further demonstrate its superior performance. Full article
(This article belongs to the Special Issue Intelligent Connected Vehicles)
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22 pages, 7903 KiB  
Article
Vehicle Localization in IoV Environments: A Vision-LSTM Approach with Synthetic Data Simulation
by Yi Liu, Jiade Jiang and Zijian Tian
Vehicles 2025, 7(1), 12; https://doi.org/10.3390/vehicles7010012 - 31 Jan 2025
Viewed by 664
Abstract
With the rapid development of the Internet of Vehicles (IoV) and autonomous driving technologies, robust and accurate visual pose perception has become critical for enabling smart connected vehicles. Traditional deep learning-based localization methods face persistent challenges in real-world vehicular environments, including occlusion, lighting [...] Read more.
With the rapid development of the Internet of Vehicles (IoV) and autonomous driving technologies, robust and accurate visual pose perception has become critical for enabling smart connected vehicles. Traditional deep learning-based localization methods face persistent challenges in real-world vehicular environments, including occlusion, lighting variations, and the prohibitive cost of collecting diverse real-world datasets. To address these limitations, this study introduces a novel approach by combining Vision-LSTM (ViL) with synthetic image data generated from high-fidelity 3D models. Unlike traditional methods reliant on costly and labor-intensive real-world data, synthetic datasets enable controlled, scalable, and efficient training under diverse environmental conditions. Vision-LSTM enhances feature extraction and classification performance through its matrix-based mLSTM modules and advanced feature aggregation strategy, effectively capturing both global and local information. Experimental evaluations in independent target scenes with distinct features and structured indoor environments demonstrate significant performance gains, achieving matching accuracies of 91.25% and 95.87%, respectively, and outperforming state-of-the-art models. These findings underscore the innovative advantages of integrating Vision-LSTM with synthetic data, highlighting its potential to overcome real-world limitations, reduce costs, and enhance accuracy and reliability for connected vehicle applications such as autonomous navigation and environmental perception. Full article
(This article belongs to the Special Issue Intelligent Connected Vehicles)
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