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WEVJ
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Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges
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Advancements in Autonomous Vehicles: Security, Optimization and Future Challenges

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 3174

Special Issue Editor

Dr. Nabil Benamar

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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 (4 papers)

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Research

15 pages, 1478 KiB  
Article
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
Viewed by 334
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 [...] Read more.
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  
Article
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 545
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 [...] Read more.
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  
Article
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 574
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 [...] Read more.
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  
Article
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 1292
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 [...] Read more.
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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