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Connected Vehicles and Vehicular Sensing in Smart Cities
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Connected Vehicles and Vehicular Sensing in Smart Cities

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".

Deadline for manuscript submissions: closed (20 January 2025) | Viewed by 4603

Special Issue Editors

Prof. Dr. Li-minn Ang (Kenneth)

E-Mail Website
Guest Editor
School of Science and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
Interests: computer; electrical and systems engineering; intelligent systems and data analytics; Internet of Things and wireless multimedia sensor systems; reconfigurable computing; innovative technologies for real-world systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jasmine Kah Phooi Seng

E-Mail Website
Guest Editor
School of AI and Advanced Computing, Xian Jiaotong Liverpool University, Suzhou 215123, China
Interests: AI and intelligent systems; data science/analytics & machine learning; big data; Internet of Things (IoT); Internet of Vehicles (IoV); edge computing; multimodal information processing; embedded intelligence & systems; mobile software development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Distributed and networked sensing and connected vehicles enable intelligent transport systems to monitor, predict, and manage traffic flow. Vehicular networking is crucial for efficient traffic management, road safety, and infotainment; as such, most modern vehicles are equipped with communication systems, computing facilities, storage, and sensing power. The integration of communication perception/Internet of Things technology in vehicles is an emerging research topic that is vital to intelligent transportation systems. As connected and autonomous vehicles and smart cities concepts become increasingly widespread, there will be a surge in research and advancements in vehicular networking, autonomous vehicles, sensor development and deployment, information fusion, decision making, traffic optimization, cyber security frameworks for the Internet of Vehicles (IoV), and applications of 5G and 6G.

This Special Issue aims to cover the most recent advances in connected vehicles, V2V communications, Vehicular Sensing, Internet of Vehicles (IoV), vehicle localization, navigation, and tracking. Potential topics include (but are not limited to):

  • Connected vehicles;
  • Vehicular sensing;
  • Vehicle positioning and localization;
  • V2V communications;
  • IoT sensing systems and vehicular networks;
  • Internet of Vehicles (IoV);
  • AI and computer vision for connected vehicles;
  • Cyber security for vehicle networks;
  • Vehicular sensing in smart cities.

Prof. Dr. Li-minn Ang (Kenneth)
Prof. Dr. Jasmine Kah Phooi Seng
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. Sensors 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 2600 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

  • connected vehicles
  • vehicular sensing
  • vehicle positioning and localization
  • V2V communications
  • IoT sensing systems and vehicular networks
  • Internet of Vehicles (IoV)
  • AI and computer vision for connected vehicles
  • cyber security for vehicle networks
  • vehicular sensing in smart cities

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

Published Papers (3 papers)

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Research

20 pages, 9475 KiB  
Article
Cross-Domain Generalization for LiDAR-Based 3D Object Detection in Infrastructure and Vehicle Environments
by Peng Zhi, Longhao Jiang, Xiao Yang, Xingzheng Wang, Hung-Wei Li, Qingguo Zhou, Kuan-Ching Li and Mirjana Ivanović
Sensors 2025, 25(3), 767; https://doi.org/10.3390/s25030767 - 27 Jan 2025
Viewed by 987
Abstract
In the intelligent transportation field, the Internet of Things (IoT) is commonly applied using 3D object detection as a crucial part of Vehicle-to-Everything (V2X) cooperative perception. However, challenges arise from discrepancies in sensor configurations between vehicles and infrastructure, leading to variations in the [...] Read more.
In the intelligent transportation field, the Internet of Things (IoT) is commonly applied using 3D object detection as a crucial part of Vehicle-to-Everything (V2X) cooperative perception. However, challenges arise from discrepancies in sensor configurations between vehicles and infrastructure, leading to variations in the scale and heterogeneity of point clouds. To address the performance differences caused by the generalization problem of 3D object detection models with heterogeneous LiDAR point clouds, we propose the Dual-Channel Generalization Neural Network (DCGNN), which incorporates a novel data-level downsampling and calibration module along with a cross-perspective Squeeze-and-Excitation attention mechanism for improved feature fusion. Experimental results using the DAIR-V2X dataset indicate that DCGNN outperforms detectors trained on single datasets, demonstrating significant improvements over selected baseline models. Full article
(This article belongs to the Special Issue Connected Vehicles and Vehicular Sensing in Smart Cities)
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14 pages, 900 KiB  
Article
Bit Error Rate Performance Improvement for Orthogonal Time Frequency Space Modulation with a Selective Decode-and-Forward Cooperative Communication Scenario in an Internet of Vehicles System
by Selman Kulaç and Müjdat Şahin
Sensors 2024, 24(16), 5324; https://doi.org/10.3390/s24165324 - 17 Aug 2024
Cited by 1 | Viewed by 1113
Abstract
Orthogonal time frequency space (OTFS) modulation has recently found its place in the literature as a much more effective waveform in time-varying channels. It is anticipated that OTFS will be widely used in the communications of smart vehicles, especially those considered within the [...] Read more.
Orthogonal time frequency space (OTFS) modulation has recently found its place in the literature as a much more effective waveform in time-varying channels. It is anticipated that OTFS will be widely used in the communications of smart vehicles, especially those considered within the scope of Internet of Things (IoT). There are efforts to obtain customized traditional point-to-point single-input single-output (SISO)-OTFS studies in the literature, but their BER performance seems a bit low. It is possible to use cooperative communications in order improve BER performance, but it is noticeable that there are very few OTFS studies in the area of cooperative communications. In this study, to the best of the authors’ knowledge, it is addressed for the first time in the literature that better performance is achieved for the OTFS waveform transmission in a selective decode-and-forward (SDF) cooperative communication scenario. In this context, by establishing a cooperative communication model consisting of a base station/source, a traffic sign/relay and a smart vehicle/destination moving at a constant speed, an end-to-end BER expression is derived. SNR-BER analysis is performed with this SDF-OTFS scheme and it is shown that a superior BER performance is achieved compared to the traditional point-to-point single-input single-output (SISO)-OTFS structure. Full article
(This article belongs to the Special Issue Connected Vehicles and Vehicular Sensing in Smart Cities)
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18 pages, 2808 KiB  
Article
Constraint Optimization Model for Dynamic Parking Space Allocation
by Abdelrahman Osman Elfaki, Wassim Messoudi, Anas Bushnag, Shakour Abuzneid and Tareq Alhmiedat
Sensors 2024, 24(12), 3988; https://doi.org/10.3390/s24123988 - 19 Jun 2024
Cited by 3 | Viewed by 1793
Abstract
Managing car parking systems is a complex process because multiple constraints must be considered; these include organizational and operational constraints. In this paper, a constraint optimization model for dynamic parking space allocation is introduced. An ad hoc algorithm is proposed, presented, and explained [...] Read more.
Managing car parking systems is a complex process because multiple constraints must be considered; these include organizational and operational constraints. In this paper, a constraint optimization model for dynamic parking space allocation is introduced. An ad hoc algorithm is proposed, presented, and explained to achieve the goal of our proposed model. This paper makes research contributions by providing an intelligent prioritization mechanism, considering user schedule shifts and parking constraints, and assigning suitable parking slots based on a dynamic distribution. The proposed model is implemented to demonstrate the applicability of our approach. A benchmark is constructed based on well-defined metrics to validate our proposed model and the results achieved. Full article
(This article belongs to the Special Issue Connected Vehicles and Vehicular Sensing in Smart Cities)
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