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Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges

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

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 13201

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Dr. Nabil Benamar

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Guest Editor

School of Technology, Moulay Ismail University of Meknes, Meknes 50050, Morocco
Interests: IoT; cloud/fog computing; python; network programmability; operating systems; computer networks and network security
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Special Issue Information

Dear colleagues,

The integration of advanced technologies into autonomous vehicles (AVs) is revolutionizing transportation by enhancing security, optimizing performance, and tackling future challenges. This Special Issue presents cutting-edge research and experimental results, ranging from robust AI algorithms that bolster security to optimization techniques that improve route planning and energy efficiency.

Key areas of interest include innovations in cybersecurity, crucial for protecting AVs from potential vulnerabilities, and advanced sensor fusion methods that enhance perception and decision-making capabilities. Intelligent control systems are also highlighted, especially those designed for safe navigation in complex environments.

This Special Issue aims to delve into future challenges such as regulatory and ethical considerations, public acceptance, and the impact on urban planning. Case studies on practical applications of AVs in urban mobility, logistics, and public transportation provide valuable insights into the current and future potential of these technologies.

The development of simulation environments and standardization efforts is critical for fostering innovation and ensuring the reliability and safety of AV systems. Furthermore, research on predictive maintenance and fault detection emphasizes the importance of advanced data analytics in enhancing the performance and longevity of AVs. This Special Issue aims to pave the way for future advancements to be made in autonomous vehicle technology.

Dr. Nabil Benamar
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. 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

autonomous vehicles
AI algorithms
cybersecurity
navigation
regulatory and ethical considerations
public acceptance
urban planning
autonomous vehicle technology

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Published Papers (11 papers)

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Research

28 pages, 2961 KiB

Open AccessArticle

Impact Assessment of Integrating AVs in Optimizing Urban Traffic Operations for Sustainable Transportation Planning in Riyadh

by Nawaf Mohamed Alshabibi

World Electr. Veh. J. 2025, 16(5), 246; https://doi.org/10.3390/wevj16050246 - 24 Apr 2025

Viewed by 208

Abstract

Integrating autonomous vehicles (AVs) into urban traffic systems presents significant opportunities for optimizing traffic flow, reducing congestion, and enhancing transportation efficiency. This study proposes a comprehensive framework that combines mathematical optimization techniques, policy planning, and AV adoption modeling to improve urban mobility. Using Highway Capacity Manual (HCM) Optimization methods, the research fine-tunes traffic signal timings, dynamically allocates green time, and enhances intersection coordination to maximize throughput. The study evaluates the impact of AV penetration on traffic flow efficiency, congestion reduction, and infrastructure readiness using real-world urban data from Riyadh. The results indicate that AV integration leads to a 40% increase in traffic throughput, a 60% reduction in congestion levels, and a 45% improvement in infrastructure readiness, highlighting the effectiveness of AV-driven traffic optimization strategies. Additionally, policy interventions aimed at reducing legal constraints and increasing societal acceptance contribute to the successful implementation of AV technology. The findings provide a data-driven roadmap for city planners and policymakers, demonstrating how a well-structured AV deployment strategy can significantly enhance urban transportation efficiency. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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20 pages, 1765 KiB

Open AccessEditor’s ChoiceArticle

Beyond Safety: Barriers to Shared Autonomous Vehicle Utilization in the Post-Adoption Phase—Evidence from Norway

by Sinuo Wu, Kristin Falk and Thor Myklebust

World Electr. Veh. J. 2025, 16(3), 133; https://doi.org/10.3390/wevj16030133 - 28 Feb 2025

Viewed by 507

Abstract

The usage rates of shared autonomous vehicles (SAVs) have become a pressing concern following their increased deployment. While prior research has focused on initial user acceptance, post-adoption behavior remains underexplored. As SAV deployment matures, public concerns have expanded beyond safety to encompass service requirements, challenging the relevance of earlier findings to current commercialization efforts. This study investigates the factors shaping SAV utilization through an empirical study in Norway, where autonomous buses have operated for several years. Through mixed methods, we first analyzed responses from 106 participants to 43 SAV users and 63 witnesses of SAV operations. The results revealed that concerns had shifted from technological anxiety to service-related factors. Through purposive interviews with individuals who showed acceptance of SAVs but did not adopt them as their primary mode of transportation, we explored the gap between high acceptance and low usage. Our findings provide insights into long-term SAV deployment and guidelines for improving usage rates, highlighting the importance of addressing service characteristics such as information transparency, vehicle appearance, speed, and convenience, rather than focusing solely on safety in commercial settings. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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20 pages, 268 KiB

Open AccessEditor’s ChoiceArticle

Legal and Safety Aspects of the Application of Automated and Autonomous Vehicles in the Republic of Croatia

by Melita Milenković, Davor Sumpor and Sandro Tokić

World Electr. Veh. J. 2025, 16(1), 34; https://doi.org/10.3390/wevj16010034 - 10 Jan 2025

Cited by 1 | Viewed by 1282

Abstract

In its draft proposal for the Road Transport Act, the Croatian government referred to European Union Directive 2022/738, which concerns the use of hired vehicles for goods transport, rather than the pertinent European Union regulations on automated and autonomous vehicles, specifically Regulation 2019/2144 and Implementing Regulation 2022/1426. This oversight highlights Croatia’s lack of preparedness to integrate highly automated and autonomous vehicles, which are crucial for safety and environmental performance as per European Union standards. This paper aims to clarify the safety and legal recommendations for the trafficking of these vehicles in Croatia. Level 2 and Level 3 automated vehicles, present in smaller numbers in road traffic in Croatia, were compared from the perspective of the lack of driving tasks and its impact on driver safety. The stages of road liability for traffic accidents were also investigated, with recommendations of strict (default) liability of manufacturers for fully autonomous vehicles as well as presumed liability of all road traffic participants for highly automated vehicles. The safety and traffic benefits of possible infrastructure upgrades for highly automated and fully autonomous vehicles were discussed, mostly in the segment of dedicated lines. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

16 pages, 603 KiB

Open AccessArticle

Understanding the Adoption of Autonomous Vehicles in China Based on TRI and TAM

by Xiuhong He, Heng Zhang, Ju Guo and Yingchun Wang

World Electr. Veh. J. 2025, 16(1), 23; https://doi.org/10.3390/wevj16010023 - 2 Jan 2025

Cited by 1 | Viewed by 1095

Abstract

Autonomous vehicles (AVs) represent a notable advancement in automotive technology, with the potential to enhance road safety, decrease energy consumption, and mitigate environmental pollution. This study aims to advance the understanding of AV development by proposing a research framework centered on the framework of “personality–perception–behavioral intention”. This framework is utilized to examine the influence of consumers’ personality traits and perceptions on their intention to adopt AVs. The research model was empirically tested using data collected from 310 questionnaires. The findings indicate that consumers’ personality traits, specifically optimism and innovativeness, along with their perception of the usefulness of AVs, exert a significant positive influence on their adoption intentions. Furthermore, the impact of these factors varies considerably across different consumer segments. Conversely, factors such as discomfort, insecurity, and perceived ease of use do not demonstrate a significant effect on the intention to adopt AVs. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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23 pages, 2065 KiB

Open AccessEditor’s ChoiceArticle

Using e³value for the Transformation of a Rent-a-Car into a Robotaxi

by João Pedro Nina Rosa, António Reis Pereira, Paulo Pinto and Miguel Mira da Silva

World Electr. Veh. J. 2025, 16(1), 16; https://doi.org/10.3390/wevj16010016 - 29 Dec 2024

Viewed by 1709

Abstract

The research objective of this paper is to analyse what is behind the self-driving offer implemented in Phoenix (Arizona) by Waymo and a normal rent-a-car company by modelling both in e³value. A gap analysis proposes a new model of the rent-a-car business with the integration of a shared autonomous vehicle ride-hailing service. The goal is to encourage the growth of additional global shared autonomous vehicle trials and their incorporation into conventional businesses. The primary objective is to enhance shared autonomous mobility options, resulting in increased road safety, decreased traffic, and decreased emissions in urban areas. As a result, modelling Waymo can serve as a foundation for expanding the use of shared autonomous vehicles by other businesses in different geographic areas. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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21 pages, 12787 KiB

Open AccessArticle

A Tractor Work Position Prediction Method Based on CNN-BiLSTM Under GNSS Signal Denial

by Yangming Hu, Liyou Xu, Xianghai Yan, Ningjie Chang, Qigang Wan and Yiwei Wu

World Electr. Veh. J. 2025, 16(1), 11; https://doi.org/10.3390/wevj16010011 - 28 Dec 2024

Viewed by 936

Abstract

In farmland environments where GNSS signals are obstructed, such as forested areas or in adverse weather conditions, traditional GNSS/INS integrated navigation systems suffer from positioning errors and instability. To address this, a model-assisted integrated navigation system is proposed, combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The CNN-BiLSTM model is trained under normal GNSS conditions and used to predict positioning when GNSS signals are interrupted, effectively replacing GNSS to ensure stable and accurate navigation. Experimental validation is conducted using field data from tractor simulations. The results show that, during a 100-s GNSS denial, the CNN-BiLSTM model reduces the average position error by 79.3% compared to pure inertial navigation and by 5.4% compared to traditional LSTM. In a 30-s GNSS denial, the average position error is reduced by 41% compared to inertial navigation and 6.2% compared to LSTM. The model maintains positioning accuracy within 3% of the GNSS/INS output under normal conditions, demonstrating its feasibility and effectiveness. This approach offers a promising solution for autonomous tractor navigation in GNSS-denied agricultural environments, contributing to precision agriculture. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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14 pages, 11023 KiB

Open AccessEditor’s ChoiceArticle

Improving Reinforcement Learning with Expert Demonstrations and Vision Transformers for Autonomous Vehicle Control

by Badr Ben Elallid, Nabil Benamar, Miloud Bagaa, Sousso Kelouwani and Nabil Mrani

World Electr. Veh. J. 2024, 15(12), 585; https://doi.org/10.3390/wevj15120585 - 19 Dec 2024

Viewed by 1204

Abstract

While IL has been successfully applied in RL-based approaches for autonomous driving, significant challenges, such as limited data for RL and poor generalization in IL, still need further investigation. To overcome these limitations, we propose in this paper a novel approach that effectively combines IL with DRL by incorporating expert demonstration data to control AV in roundabout and right-turn intersection scenarios. Instead of employing CNNs, we integrate a ViT into the perception module of the SAC algorithm to extract key features from environmental images. The ViT algorithm excels in identifying relationships across different parts of an image, thereby enhancing environmental understanding, which leads to more accurate and precise decision making. Consequently, our approach not only boosts the performance of the DRL model but also accelerates its convergence, improving the overall efficiency and effectiveness of AVs in roundabouts and right-turn intersections with dense traffic by a achieving high success rate and low collision compared to RL baseline algorithms. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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15 pages, 1478 KiB

Open AccessEditor’s ChoiceArticle

Tapping the Brakes: An Exploratory Survey of Consumers’ Perceptions of Autonomous Vehicles

by George D. Shows, Mathew Zothner and Pia A. Albinsson

World Electr. Veh. J. 2024, 15(11), 530; https://doi.org/10.3390/wevj15110530 - 18 Nov 2024

Cited by 1 | Viewed by 1105

Abstract

The purpose of this study is to gain a better understanding of the difficulty in measuring consumer acceptance of emergent technologies where artificial intelligence is present in autonomous vehicles (AVs). Using the Technology Acceptance Model (TAM) as our theoretical lens, survey data of US adult consumers are used to better understand consumer acceptance of AVs. Results from Partial Least Squares–Structural Equation Modeling (PLS-SEM) show that the certainty of product performance and interest are positively related to usage. Surprisingly, the relationship between two variables, internal locus of control and ease of use and usage, was not significant, which could be explained by AVs being self-driving and the ease of use therefore not being important in this context. Internal locus of control was negatively related to willingness to buy, and interest and usage were positively related to willingness to buy. Mediation analysis further explains these relationships. This research calls into question the TAM, long used as a measurement for the acceptance of information systems, as an acceptable model for measuring consumer acceptance where the intent is to purchase technology that contains artificial intelligence. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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15 pages, 2771 KiB

Open AccessArticle

Vehicle Lane Changing Game Model Based on Improved SVM Algorithm

by Jian Wang, Hongxiang Wang, Mingzhe Fei and Gang Zhou

World Electr. Veh. J. 2024, 15(11), 505; https://doi.org/10.3390/wevj15110505 - 4 Nov 2024

Viewed by 1128

Abstract

In order to improve the autonomous lane-changing performance of unmanned vehicles, this paper aims to solve the problem of inaccurate decision classification in traditional support vector machine (SVM) algorithms applied to the lane-changing decision-making stage of intelligent driving vehicles. By using game theory-related theories and combining the improved support vector machine (SSA-SVM) method, a vehicle autonomous lane-changing strategy based on game theory is established. The optimized SVM method has certain advantages for vehicle lane-changing decision-making with a small sample size in actual production processes. The lane-changing decision judgment accuracy rate of the SSA-SVM algorithm model can reach 93.6% compared with the SVM algorithm model without algorithm optimization; the SSA-SVM algorithm model has obvious advantages in decision performance and running speed. Therefore, the proposed new algorithm can effectively solve the problem of the objective consideration of the payoff function in conventional decision game theory. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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17 pages, 1614 KiB

Open AccessEditor’s ChoiceArticle

Evaluating a Reference Model for SAV in Urban Areas

by Antonio Reis Pereira, Pedro Portela, Marta Bicho and Miguel Mira da Silva

World Electr. Veh. J. 2024, 15(11), 491; https://doi.org/10.3390/wevj15110491 - 28 Oct 2024

Viewed by 1095

Abstract

Previous work presented a reference model for shared autonomous vehicles in urban areas supported by a systematic literature review and topic modeling. The proposed reference model was then evaluated with two real-world demonstrations: the service provided by Waymo in Phoenix and another offered by Baidu in Beijing. In this paper, we present another evaluation based on a survey conducted with a group of potential stakeholders belonging to the mobility industry who were asked about their agreement with each of the concepts in the reference model. The resulting artifact is stronger and more reliable because it reflects the feedback of mobility experts. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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14 pages, 2331 KiB

Open AccessArticle

Enhancing Weather Scene Identification Using Vision Transformer

by Christine Dewi, Muhammad Asad Arshed, Henoch Juli Christanto, Hafiz Abdul Rehman, Amgad Muneer and Shahzad Mumtaz

World Electr. Veh. J. 2024, 15(8), 373; https://doi.org/10.3390/wevj15080373 - 16 Aug 2024

Viewed by 2033

Abstract

The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life highlights the vital necessity for accurate information. Precise weather detection is especially crucial for industries like intelligent transportation, outside vision systems, and driverless cars. The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate. Unmatched accuracy is required for local weather scene forecasting in real time. This work utilizes the capabilities of computer vision to address these important issues. Specifically, we employ the advanced Vision Transformer model to distinguish between 11 different weather scenarios. The development of this model results in a remarkable performance, achieving an accuracy rate of 93.54%, surpassing industry standards such as MobileNetV2 and VGG19. These findings advance computer vision techniques into new domains and pave the way for reliable weather scene recognition systems, promising extensive real-world applications across various industries. Full article

(This article belongs to the Special Issue Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges)

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