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WEVJ
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Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities
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Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6729

Special Issue Editors

Dr. Manuela Montangero

E-Mail Website
Guest Editor
Dipartimento di Scienze Fisiche, Informatiche e Matematiche, Università di Modena and Reggio Emilia, 41125 Modena, Italy
Interests: algorithm design; combinatorial optimization algorithms; parallel and distributed computing; vehicle coordination; Internet of Things (IoT); IoT-enabling technologies; communications; routing protocol; internet parallel and distributed computing; multi-agent system; task assignment; scheduling response time
Special Issues, Collections and Topics in MDPI journals
Dr. Gianluca De Marco

E-Mail Website
Guest Editor
Dipartimento di Informatica, Università di Salerno, 84084 Fisciano, SA, Italy
Interests: distributed computer systems; algorithm design; parallel and distributed computing; message complexity; leader election; failure detection; fault tolerance; channel utilization; contention resolution

Special Issue Information

Dear Colleagues,

With the rapid advancements in autonomous and electric vehicle technologies, the transportation landscape in urban areas is witnessing a significant transformations. As vehicle technologies continue to evolve, they offer promising solutions for sustainable urban transportation. Smart cities, with their integrated infrastructure and data-driven capabilities, present an ideal environment to address the challenges of managing urban mobility efficiently and opportunities for dynamic traffic management. Real-time data from various sources can be utilized to optimize traffic flow, manage signals, and improve overall transportation efficiency. Leveraging smart city infrastructures can alleviate congestion, enhance mobility, and reduce pollution.

Effective traffic management policies are essential for the successful integration of autonomous and non-connected vehicles. Indeed, while autonomous vehicles hold promise for the future, there will be a transitional period with both connected and non-connected vehicles on the roads. Ensuring safe interactions between these vehicle types is vital. These policies can address safety concerns, optimize traffic patterns, and enhance the livability of urban spaces. Balancing the coexistence of autonomous and traditional vehicles requires innovative approaches and seamless coordination to ensure safety and efficiency on the roads.

This Special Issue seeks to explore the potential of coordinating autonomous and electric vehicles within the context of smart cities. We invite researchers to submit original research, theoretical and experimental results, case studies, and review articles related to the coordination of vehicles in smart cities. Topics of interest include (but are not limited to):

  • Traffic management strategies in smart cities;
  • Integration of autonomous and electric vehicles into urban transportation systems;
  • Data-driven and AI approaches for traffic optimization;
  • Policy frameworks for managing autonomous and non-connected vehicles;
  • Impact of coordinated vehicle systems on urban sustainability;
  • Case studies and real-world implementations in smart cities.

Dr. Manuela Montangero
Dr. Gianluca De Marco
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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

  • smart cities
  • vehicles coordination
  • autonomous vehicles
  • electric vehicles
  • IoT
  • traffic management
  • urban mobility

Published Papers (6 papers)

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Research

19 pages, 10426 KiB  
Article
Leveraging 5G Technology to Investigate Energy Consumption and CPU Load at the Edge in Vehicular Networks
by Salah Eddine Merzougui, Xhulio Limani, Andreas Gavrielides, Claudio Enrico Palazzi and Johann Marquez-Barja
World Electr. Veh. J. 2024, 15(4), 171; https://doi.org/10.3390/wevj15040171 - 19 Apr 2024
Viewed by 378
Abstract
The convergence of vehicular communications, 5th generation mobile network (5G) technology, and edge computing marks a paradigm shift in intelligent transportation. Vehicular communication systems, including Vehicle-to-Vehicle and Vehicle-to-Infrastructure, are integral to Intelligent Transportation Systems. The advent of 5G enhances connectivity, while edge computing [...] Read more.
The convergence of vehicular communications, 5th generation mobile network (5G) technology, and edge computing marks a paradigm shift in intelligent transportation. Vehicular communication systems, including Vehicle-to-Vehicle and Vehicle-to-Infrastructure, are integral to Intelligent Transportation Systems. The advent of 5G enhances connectivity, while edge computing brings computational processes closer to data sources. This synergy holds the potential to revolutionize transportation efficiency and safety. This research investigates vehicular communication and edge computing dynamics within a 5G network, considering varying distances between On Board Units and Roadside Units. Energy consumption patterns and CPU load at the RSU are analyzed through meticulous real-world experiments and simulations. Our results show stable energy consumption at shorter distances, with fluctuations increasing at greater ranges. CPU load correlates with communication distance, highlighting the need for adaptive algorithms. While experiments exhibit higher variability, our simulations validate these findings, emphasizing the importance of considering transmission range in vehicular communication network design. Full article
(This article belongs to the Special Issue Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities)
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16 pages, 15476 KiB  
Article
Collaborative Misbehaviour Response System for Improving Road Safety
by Khaled Chikh, Chinmay Satish Shrivastav and Roberto Cavicchioli
World Electr. Veh. J. 2024, 15(4), 158; https://doi.org/10.3390/wevj15040158 - 10 Apr 2024
Viewed by 506
Abstract
This paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like [...] Read more.
This paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like ADAS, edge computing, and cloud services to enhance road safety. Upon detection of misbehaviour, the MBR system utilizes data from interconnected parking facilities to identify the nearest safe location and provides navigation guidance to authorities and nearby vehicles. The paper presents a prototype of the MBR system, demonstrating its efficiency in detecting misbehaviours and coordinating swift responses. It also discusses the system’s limitations and societal implications, outlining future research directions, including integration with autonomous vehicle systems and variable speed limit technologies, to further improve road safety through proactive and context-aware response mechanisms. Full article
(This article belongs to the Special Issue Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities)
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19 pages, 5620 KiB  
Article
A Study on Reducing Traffic Congestion in the Roadside Unit for Autonomous Vehicles Using BSM and PVD
by Sangmin Lee, Jinhyeok Oh, Minchul Kim, Myongcheol Lim, Keon Yun, Heesun Yun, Chanmin Kim and Juntaek Lee
World Electr. Veh. J. 2024, 15(3), 117; https://doi.org/10.3390/wevj15030117 - 18 Mar 2024
Viewed by 1148
Abstract
With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. [...] Read more.
With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs’ capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work’s current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs’ capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety. Full article
(This article belongs to the Special Issue Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities)
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15 pages, 2622 KiB  
Article
Statistical Risk and Performance Analyses on Naturalistic Driving Trajectory Datasets for Traffic Modeling
by Ruixue Zong, Ying Wang, Juan Ding and Weiwen Deng
World Electr. Veh. J. 2024, 15(3), 77; https://doi.org/10.3390/wevj15030077 - 21 Feb 2024
Viewed by 1125
Abstract
The development of autonomous driving technology has made simulation testing one of the most important tools for evaluating system performance. However, there is a lack of systematic methods for analyzing and assessing naturalistic driving trajectory datasets. Specifically, there is a lack of comprehensive [...] Read more.
The development of autonomous driving technology has made simulation testing one of the most important tools for evaluating system performance. However, there is a lack of systematic methods for analyzing and assessing naturalistic driving trajectory datasets. Specifically, there is a lack of comprehensive analyses on data diversity and balance in machine learning-oriented research. This study presents a comprehensive assessment of existing highway scenario datasets in the context of traffic modeling in autonomous driving simulation tests. In order to clarify the level of traffic risk, we design a systematic risk index and propose an index describing the degree of data scatter based on the principle of Euclidean distance quantization. By comparing several datasets, including NGSIM, highD, INTERACTION, CitySim, and our self-collected Highway dataset, we find that the proposed metrics can effectively quantify the risk level of the dataset while helping to gain insight into the diversity and balance differences of the dataset. Full article
(This article belongs to the Special Issue Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities)
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34 pages, 7106 KiB  
Article
Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach
by Pannee Suanpang and Pitchaya Jamjuntr
World Electr. Veh. J. 2024, 15(2), 67; https://doi.org/10.3390/wevj15020067 - 14 Feb 2024
Viewed by 1338
Abstract
As global awareness for preserving natural energy sustainability rises, electric vehicles (EVs) are increasingly becoming a preferred choice for transportation because of their ability to emit zero emissions, conserve energy, and reduce pollution, especially in smart cities with sustainable development. Nonetheless, the lack [...] Read more.
As global awareness for preserving natural energy sustainability rises, electric vehicles (EVs) are increasingly becoming a preferred choice for transportation because of their ability to emit zero emissions, conserve energy, and reduce pollution, especially in smart cities with sustainable development. Nonetheless, the lack of adequate EV charging infrastructure remains a significant problem that has resulted in varying charging demands at different locations and times, particularly in developing countries. As a consequence, this inadequacy has posed a challenge for EV drivers, particularly those in smart cities, as they face difficulty in locating suitable charging stations. Nevertheless, the recent development of deep reinforcement learning is a promising technology that has the potential to improve the charging experience in several ways over the long term. This paper proposes a novel approach for recommending EV charging stations using multi-agent reinforcement learning (MARL) algorithms by comparing several popular algorithms, including the deep deterministic policy gradient, deep Q-network, multi-agent DDPG (MADDPG), Real, and Random, in optimizing the placement and allocation of the EV charging stations. The results demonstrated that MADDPG outperformed other algorithms in terms of the Mean Charge Waiting Time, CFT, and Total Saving Fee, thus indicating its superiority in addressing the EV charging station problem in a multi-agent setting. The collaborative and communicative nature of the MADDPG algorithm played a key role in achieving these results. Hence, this approach could provide a better user experience, increase the adoption of EVs, and be extended to other transportation-related problems. Overall, this study highlighted the potential of MARL as a powerful approach for solving complex optimization problems in transportation and beyond. This would also contribute to the development of more efficient and sustainable transportation systems in smart cities for sustainable development. Full article
(This article belongs to the Special Issue Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities)
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24 pages, 4003 KiB  
Article
Public Perception of the Introduction of Autonomous Vehicles
by Abdulaziz Aldakkhelallah, Abdulrahman S. Alamri, Stelios Georgiou and Milan Simic
World Electr. Veh. J. 2023, 14(12), 345; https://doi.org/10.3390/wevj14120345 - 12 Dec 2023
Cited by 1 | Viewed by 1632
Abstract
Autonomous vehicles (AVs) will transform transport, but public opinion will play a key role in decisions on how widely and quickly they are adopted. The purpose of the study presented here was to investigate community’s views on that transition. As a method for [...] Read more.
Autonomous vehicles (AVs) will transform transport, but public opinion will play a key role in decisions on how widely and quickly they are adopted. The purpose of the study presented here was to investigate community’s views on that transition. As a method for primary data collection on public awareness, attitudes, and readiness to use autonomous cars, survey was conducted in Saudi Arabia. Following that, we used statistical tools to analyse responses. Our findings indicate that the participants are largely receptive to using new technologies and had favourable attitudes towards the transition. Ordinal logistic regression model showed a wide variation in public opinion regarding the expected benefits that may accompany the transition. Our findings reveal that awareness of AVs’ benefits is positively correlated with the age of participants. Perceived costs on one side, and convenience and safety on the other, were found to have had a substantial impact on the opinions of the participants. Investigation presented here shows a sample of the public’s perception of AVs in Saudi Arabia. This can guide the development of AVs and their deployment in that region as well as worldwide. Full article
(This article belongs to the Special Issue Autonomous Electric Vehicles Combined with Non-connected Vehicles in Smart Cities)
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