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Electronics
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Autonomous and Connected Vehicles
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Autonomous and Connected Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 12609

Special Issue Editor

Dr. Piotr Borkowski

E-Mail Website
Guest Editor
Faculty of Computer Science and Telecommunications, Maritime University of Szczecin, 70500 Szczecin, Poland
Interests: artificial intelligence in navigation; computer science in navigation; automation and control systems in navigation; control of ship motion; autonomous ships

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to bring together researchers and practitioners involved in the autonomous vehicles field. Autonomy in vehicles is broadly understood as connected vehicles, vehicles with navigational decision support system, vehicles with a limited crew, remotely controlled vehicles, and fully autonomous vehicles. In this Special Issue, articles describing innovative discoveries, methods, systems, and solutions that impact the advancement of vehicle autonomy, especially in the field of navigation, are welcomed.

The topics of particular interest include, but are not limited to:

  • Autonomous vehicles;
  • Connected vehicles;
  • Autonomous navigation;
  • The application of artificial intelligence methods in autonomous vehicles;
  • Autonomous vehicle control systems;
  • Expert systems in autonomous vehicles;
  • Navigational decision support system;
  • Computational mathematics in navigation;
  • Cybersecurity in autonomous vehicles.

Dr. Piotr Borkowski
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. Electronics 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 2400 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
  • connected vehicles
  • autonomous navigation
  • artificial intelligence
  • control systems
  • expert systems
  • decision support system
  • computational mathematics
  • cybersecurity

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Published Papers (6 papers)

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Research

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27 pages, 7600 KiB  
Article
Spiking Neural Networks for Real-Time Pedestrian Street-Crossing Detection Using Dynamic Vision Sensors in Simulated Adverse Weather Conditions
by Mustafa Sakhai, Szymon Mazurek, Jakub Caputa, Jan K. Argasiński and Maciej Wielgosz
Electronics 2024, 13(21), 4280; https://doi.org/10.3390/electronics13214280 - 31 Oct 2024
Viewed by 633
Abstract
This study explores the integration of Spiking Neural Networks (SNNs) with Dynamic Vision Sensors (DVSs) to enhance pedestrian street-crossing detection in adverse weather conditions—a critical challenge for autonomous vehicle systems. Utilizing the high temporal resolution and low latency of DVSs, which excel in [...] Read more.
This study explores the integration of Spiking Neural Networks (SNNs) with Dynamic Vision Sensors (DVSs) to enhance pedestrian street-crossing detection in adverse weather conditions—a critical challenge for autonomous vehicle systems. Utilizing the high temporal resolution and low latency of DVSs, which excel in dynamic, low-light, and high-contrast environments, this research evaluates the effectiveness of SNNs compared to traditional Convolutional Neural Networks (CNNs). The experimental setup involved a custom dataset from the CARLA simulator, designed to mimic real-world variability, including rain, fog, and varying lighting conditions. Additionally, the JAAD dataset was adopted to allow for evaluations using real-world data. The SNN models were optimized using Temporally Effective Batch Normalization (TEBN) and benchmarked against well-established deep learning models, concerning their accuracy, computational efficiency, and energy efficiency in complex weather conditions. This study also conducted a comprehensive analysis of energy consumption, highlighting the significant reduction in energy usage achieved by SNNs when processing DVS data. The results indicate that SNNs, when integrated with DVSs, not only reduce computational overhead but also dramatically lower energy consumption, making them a highly efficient choice for real-time applications in autonomous vehicles (AVs). Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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28 pages, 1782 KiB  
Article
Searching for a Cheap Robust Steering Controller
by Trevor Vidano and Francis Assadian
Electronics 2024, 13(10), 1908; https://doi.org/10.3390/electronics13101908 - 13 May 2024
Viewed by 1045
Abstract
The study of lateral steering control for Automated Driving Systems identifies new control solutions more often than new control problems. This is likely due to the maturity of the field. To prevent repeating efforts toward solving already-solved problems, what is needed is a [...] Read more.
The study of lateral steering control for Automated Driving Systems identifies new control solutions more often than new control problems. This is likely due to the maturity of the field. To prevent repeating efforts toward solving already-solved problems, what is needed is a cohesive way of evaluating all developed controllers under a wide variety of environmental conditions. This work serves as a step in this direction. Four controllers are tested on five maneuvers representing highways and collision avoidance trajectories. Each controller and maneuver combination is repeated on five sets of environmental conditions or Operational Design Domains (ODDs). The design of these ODDs ensures the translation of these experimental results to real-world applications. The commercial software, CarSim 2020, is extended with Simulink models of the environment, sensor dynamics, and state estimation performances to perform highly repeatable and realistic evaluations of each controller. The results of this work demonstrate that most of the combinations of maneuvers and ODDs have existing cheap controllers that achieve satisfactorily safe performance. Therefore, this field’s research efforts should be directed toward finding new control problems in lateral path tracking rather than proposing new controllers for ODDs that are already solved. Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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23 pages, 27139 KiB  
Article
Enhancing the Safety of Autonomous Vehicles in Adverse Weather by Deep Learning-Based Object Detection
by Biwei Zhang, Murat Simsek, Michel Kulhandjian and Burak Kantarci
Electronics 2024, 13(9), 1765; https://doi.org/10.3390/electronics13091765 - 2 May 2024
Viewed by 1914
Abstract
Recognizing and categorizing items in weather-adverse environments poses significant challenges for autonomous vehicles. To improve the robustness of object-detection systems, this paper introduces an innovative approach for detecting objects at different levels by leveraging sensors and deep learning-based solutions within a traffic circle. [...] Read more.
Recognizing and categorizing items in weather-adverse environments poses significant challenges for autonomous vehicles. To improve the robustness of object-detection systems, this paper introduces an innovative approach for detecting objects at different levels by leveraging sensors and deep learning-based solutions within a traffic circle. The suggested approach improves the effectiveness of single-stage object detectors, aiming to advance the performance in perceiving autonomous racing environments and minimizing instances of false detection and low recognition rates. The improved framework is based on the one-stage object-detection model, incorporating multiple lightweight backbones. Additionally, attention mechanisms are integrated to refine the object-detection process further. Our proposed model demonstrates superior performance compared to the state-of-the-art method on the DAWN dataset, achieving a mean average precision (mAP) of 99.1%, surpassing the previous result of 84.7%. Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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18 pages, 1111 KiB  
Article
Control Performance Requirements for Automated Driving Systems
by Trevor Vidano and Francis Assadian
Electronics 2024, 13(5), 902; https://doi.org/10.3390/electronics13050902 - 27 Feb 2024
Cited by 2 | Viewed by 1256
Abstract
This research investigates the development of risk-based performance requirements for the control of an automated driving system (ADS). The proposed method begins by determining the target level of safety for the virtual driver of an ADS. The underlying assumptions are informed by existing [...] Read more.
This research investigates the development of risk-based performance requirements for the control of an automated driving system (ADS). The proposed method begins by determining the target level of safety for the virtual driver of an ADS. The underlying assumptions are informed by existing data. Next, geometric models of the road and vehicle are used to derive deterministic performance levels of the virtual driver. To integrate the risk and performance requirements seamlessly, we propose new definitions for errors associated with the planner, pose, and control modules. These definitions facilitate the derivation of stochastic performance requirements for each module, thus ensuring an overall target level of safety. Notably, these definitions enable real-time controller performance monitoring, thus potentially enabling fault detection linked to the system’s overall safety target. At a high level, this approach argues that the requirements for the virtual driver’s modules should be designed simultaneously. To illustrate this approach, this technique is applied to a research project available in the literature that developed an automated steering system for an articulated bus. This example shows that the method generates achievable performance requirements that are verifiable through experimental testing and highlights the importance in validating the underlying assumptions for effective risk management. Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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Review

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26 pages, 1199 KiB  
Review
A Critical AI View on Autonomous Vehicle Navigation: The Growing Danger
by Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka, Piotr Borkowski and Adrianna Łobodzińska
Electronics 2024, 13(18), 3660; https://doi.org/10.3390/electronics13183660 - 14 Sep 2024
Viewed by 2445
Abstract
Autonomous vehicles (AVs) represent a transformative advancement in transportation technology, promising to enhance travel efficiency, reduce traffic accidents, and revolutionize our road systems. Central to the operation of AVs is the integration of artificial intelligence (AI), which enables these vehicles to navigate complex [...] Read more.
Autonomous vehicles (AVs) represent a transformative advancement in transportation technology, promising to enhance travel efficiency, reduce traffic accidents, and revolutionize our road systems. Central to the operation of AVs is the integration of artificial intelligence (AI), which enables these vehicles to navigate complex environments with minimal human intervention. This review critically examines the potential dangers associated with the increasing reliance on AI in AV navigation. It explores the current state of AI technologies, highlighting key techniques such as machine learning and neural networks, and identifies significant challenges including technical limitations, safety risks, and ethical and legal concerns. Real-world incidents, such as Uber’s fatal accident and Tesla’s crash, underscore the potential risks and the need for robust safety measures. Future threats, such as sophisticated cyber-attacks, are also considered. The review emphasizes the importance of improving AI systems, implementing comprehensive regulatory frameworks, and enhancing public awareness to mitigate these risks. By addressing these challenges, we can pave the way for the safe and reliable deployment of autonomous vehicles, ensuring their benefits can be fully realized. Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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21 pages, 551 KiB  
Review
Cybersecurity in Autonomous Vehicles—Are We Ready for the Challenge?
by Irmina Durlik, Tymoteusz Miller, Ewelina Kostecka, Zenon Zwierzewicz and Adrianna Łobodzińska
Electronics 2024, 13(13), 2654; https://doi.org/10.3390/electronics13132654 - 6 Jul 2024
Cited by 2 | Viewed by 3845
Abstract
The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence [...] Read more.
The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence (AI). This review examines the current state of cybersecurity in autonomous vehicles, identifying major threats such as remote hacking, sensor manipulation, data breaches, and denial of service (DoS) attacks. It also explores existing countermeasures including intrusion detection systems (IDSs), encryption, over-the-air (OTA) updates, and authentication protocols. Despite these efforts, numerous challenges remain, including the complexity of AV systems, lack of standardization, latency issues, and resource constraints. This review concludes by highlighting future directions in cybersecurity research and development, emphasizing the potential of AI and machine learning, blockchain technology, industry collaboration, and legislative measures to enhance the security of autonomous vehicles. Full article
(This article belongs to the Special Issue Autonomous and Connected Vehicles)
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