Submit to Electronics Review for Electronics Propose a Special Issue

Journal Menu

Journal Browser

Advancements in Connected and Autonomous Vehicles

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 22863

Share This Special Issue

Special Issue Editor

Dr. Arman Sargolzaei

E-Mail Website
Guest Editor

Department of Mechanical Engineering, University of South Florida, Tampa, FL 33620, USA
Interests: security of networked control systems; safety and security of connected and autonomous vehicles; nonlinear control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Connected autonomous vehicles (CAVs) hold immense promise in revolutionizing transportation, offering benefits in energy efficiency, crash prevention, traffic management, etc. Through real-time data exchange, CAVs optimize routes and driving behavior, resulting in reduced fuel consumption and emissions, while their interconnectedness enables crash prediction and prevention, potentially saving countless lives. Furthermore, CAVs have the potential to alleviate traffic congestion by coordinating movement and streamlining traffic flow.

However, the realization of these advantages is not without its challenges. Ensuring the safety and security of CAVs is a top priority, as they are susceptible to cyberattacks, which could have catastrophic consequences. Rigorous testing and verification of autonomous driving algorithms are essential for guaranteeing their reliability and performance under diverse conditions. Moreover, the implementation of CAVs faces legal, regulatory, and ethical hurdles, necessitating the establishment of new standards and protocols for seamless integration into existing infrastructure. Another significant challenge lies in the readiness of the infrastructure to support CAVs effectively. Existing roads and transportation systems may require extensive upgrades and adaptations to accommodate the unique needs of CAVs. This includes the installation of smart sensors, communication networks, and the establishment of vehicle-to-infrastructure (V2I) communication capabilities. Additionally, ensuring interoperability among different CAV systems and infrastructure elements is crucial to achieving a cohesive and efficient transportation network.

In light of these challenges, this Special Issue welcomes researchers to contribute their valuable findings to address the multifaceted issues surrounding CAVs. Research focusing on security solutions, safety standards, testing methodologies, verification techniques, energy-efficient solutions, and infrastructure readiness will play a pivotal role in unlocking the full potential of CAVs. By surmounting these challenges, researchers can pave the way for a safer, more energy-efficient, and seamlessly integrated autonomous future in transportation.

Dr. Arman Sargolzaei
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

testing and verification of CAVs
security of CAVs
energy efficiency of CAVs
safe and secure control design for cooperative driving algorithms
infrastructure readiness for CAVs
policies and standards
ethical challenges

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

22 pages, 5344 KiB

Open AccessArticle

Research on Calibration Method of Triaxial Magnetometer Based on Improved PSO-Ellipsoid Fitting Algorithm

by Jun Guan, Zhihui Chen and Guilin Jiang

Electronics 2025, 14(9), 1778; https://doi.org/10.3390/electronics14091778 - 27 Apr 2025

Viewed by 289

Abstract

To address the measurement accuracy degradation of triaxial magnetometers caused by manufacturing errors and environmental interference, and the limited robustness of traditional calibration methods, this study proposes a Dynamic Hierarchical Elite-guided Particle Swarm Optimization (DHEPSO)-based ellipsoid fitting algorithm. First, an error model for the triaxial magnetometers is established. Next, the DHEPSO algorithm is utilized to fit the ellipsoid parameters by integrating a dynamic hierarchical mechanism, elite guidance strategy, and adaptive inertia weight adjustment, thereby balancing global exploration and local exploitation to efficiently optimize the parameters. Finally, error compensation and precise calibration are achieved using the optimized parameters. The simulation results show that, compared to the Least Squares Method (LSM), it reduces the absolute distance between the simulated data and the ellipsoid by 63.10% and the post-calibration total magnetic field intensity standard deviation by 60% under outlier interference. Against the traditional PSO, TSLPSO, MPSO, and AWPSO, DHEPSO achieves total distance reductions of 48.52%, 47.74%, 56.71%, and 33.09%, respectively, with faster convergence. The statistical analysis of 60 trials confirms DHEPSO’s stability, exhibiting lower median error and interquartile range. The results validate DHEPSO’s high precision and robustness in high-noise environments, offering theoretical support for engineering applications. Full article

(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)

► Show Figures

Figure 1

37 pages, 4815 KiB

Open AccessEditor’s ChoiceArticle

Autonomous Forklifts: State of the Art—Exploring Perception, Scanning Technologies and Functional Systems—A Comprehensive Review

by Muftah A Fraifer, Joseph Coleman, James Maguire, Petar Trslić, Gerard Dooly and Daniel Toal

Electronics 2025, 14(1), 153; https://doi.org/10.3390/electronics14010153 - 2 Jan 2025

Cited by 3 | Viewed by 3873

Abstract

This paper presents a comprehensive overview of cutting-edge autonomous forklifts, with a strong emphasis on sensors, object detection and system functionality. It aims to explore how this technology is evolving and where it is likely headed in both the near and long-term future, while also highlighting the latest developments in both academic research and industrial applications. Given the critical importance of object detection and recognition in machine vision and autonomous vehicles, this area receives particular attention. The article provides an in-depth summary of both commercial and prototype forklifts, discussing key aspects such as design features, capabilities and benefits, and offers a detailed technical comparison. Specifically, it clarifies that all available data pertains to commercially available forklifts. To obtain a better understanding of the current state-of-the-art and its limitations, the analysis also reviews commercially available autonomous forklifts. Finally, this paper includes a comprehensive bibliography of research findings in this field. Full article

(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)

► Show Figures

Figure 1

34 pages, 9890 KiB

Open AccessArticle

Synchronized Delay Measurement of Multi-Stream Analysis over Data Concentrator Units

by Anvarjon Yusupov, Sun Park and JongWon Kim

Electronics 2025, 14(1), 81; https://doi.org/10.3390/electronics14010081 - 27 Dec 2024

Viewed by 1027

Abstract

Autonomous vehicles (AVs) rely heavily on multi-modal sensors to perceive their surroundings and make real-time decisions. However, the increasing complexity of these sensors, combined with the computational demands of AI models and the challenges of synchronizing data across multiple inputs, presents significant obstacles for AV systems. These challenges of the AV domain often lead to performance latency, resulting in delayed decision-making, causing major traffic accidents. The data concentrator unit (DCU) concept addresses these issues by optimizing data pipelines and implementing intelligent control mechanisms to process sensor data efficiently. Identifying and addressing bottlenecks that contribute to latency can enhance system performance, reducing the need for costly hardware upgrades or advanced AI models. This paper introduces a delay measurement tool for multi-node analysis, enabling synchronized monitoring of data pipelines across connected hardware platforms, such as clock-synchronized DCUs. The proposed tool traces the execution flow of software applications and assesses time delays at various stages of the data pipeline in clock-synchronized hardware. The various stages are represented with intuitive graphical visualization, simplifying the identification of performance bottlenecks. Full article

(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)

► Show Figures

Figure 1

18 pages, 7185 KiB

Open AccessArticle

Assessing Satellite-Augmented Connected Vehicle Technology for Security Credentials and Traveler Information Delivery

by Sisinnio Concas and Vishal C. Kummetha

Electronics 2024, 13(22), 4444; https://doi.org/10.3390/electronics13224444 - 13 Nov 2024

Viewed by 771

Abstract

Vehicle-to-Everything (V2X) technology has the capability to enhance road safety by enabling wireless exchange of telematics and spatiotemporal information between connected vehicles (CVs). Effective V2X communication depends on rapid information sharing between Roadside Units (RSUs), in-vehicle On-Board Units (OBUs), and other connected infrastructure. However, there are increasing concerns with RSUs related to installation needs, reliability, and coverage, especially on rural roadways. This study aims to evaluate the benefits of augmenting CV infrastructure with satellite technology in situations where RSU access or coverage is limited while maintaining V2X security protocols and critical information exchange. The study utilizes data from over 400 personal, fleet, and commercial CVs collected during two real-world pilot deployments in the United States, one in an urban environment in Florida and one in a rural environment in Wyoming. The analysis performed shows that the delivery of critical security credential information and traveler information messages (TIMs) to CVs is dependent on a multitude of environmental and operational reliability factors. Overall, information delivery is faster with dense RSU infrastructure as compared to satellites. However, we show that by augmenting RSU infrastructure with satellite technology, the delivery of information is more robust, improving V2X system reliability, security, and overall road safety. Full article

(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)

► Show Figures

Figure 1

14 pages, 1073 KiB

Open AccessArticle

Fast and Lightweight Vision-Language Model for Adversarial Traffic Sign Detection

by Furkan Mumcu and Yasin Yilmaz

Electronics 2024, 13(11), 2172; https://doi.org/10.3390/electronics13112172 - 3 Jun 2024

Cited by 1 | Viewed by 1818

Abstract

Several attacks have been proposed against autonomous vehicles and their subsystems that are powered by machine learning (ML). Road sign recognition models are especially heavily tested under various adversarial ML attack settings, and they have proven to be vulnerable. Despite the increasing research on adversarial ML attacks against road sign recognition models, there is little to no focus on defending against these attacks. In this paper, we propose the first defense method specifically designed for autonomous vehicles to detect adversarial ML attacks targeting road sign recognition models, which is called ViLAS (Vision-Language Model for Adversarial Traffic Sign Detection). The proposed defense method is based on a custom, fast, lightweight, and salable vision-language model (VLM) and is compatible with any existing traffic sign recognition system. Thanks to the orthogonal information coming from the class label text data through the language model, ViLAS leverages image context in addition to visual data for highly effective attack detection performance. In our extensive experiments, we show that our method consistently detects various attacks against different target models with high true positive rates while satisfying very low false positive rates. When tested against four state-of-the-art attacks targeting four popular action recognition models, our proposed detector achieves an average AUC of 0.94. This result achieves a 25.3% improvement over a state-of-the-art defense method proposed for generic image attack detection, which attains an average AUC of 0.75. We also show that our custom VLM is more suitable for an autonomous vehicle compared to the popular off-the-shelf VLM and CLIP in terms of speed (4.4 vs. 9.3 milliseconds), space complexity (0.36 vs. 1.6 GB), and performance (0.94 vs. 0.43 average AUC). Full article

(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)

► Show Figures

Figure 1

16 pages, 3071 KiB

Open AccessArticle

A Nonlinear Control Design for Cooperative Adaptive Cruise Control with Time-Varying Communication Delay

by Parisa Ansari Bonab and Arman Sargolzaei

Electronics 2024, 13(10), 1875; https://doi.org/10.3390/electronics13101875 - 10 May 2024

Cited by 4 | Viewed by 1849

Abstract

Cooperative adaptive cruise control (CACC) is one of the main features of connected and autonomous vehicles (CAVs), which uses connectivity to improve the efficiency of adaptive cruise control (ACC). The addition of reliable communication systems to ACC reduces fuel consumption, maximizes road capacity, and ensures traffic safety. However, the performance, stability, and safety of CACC could be affected by the transmission of outdated data caused by communication delays. This paper proposes a Lyapunov-based nonlinear controller to mitigate the impact of time-varying delays in the communication channel of CACC. This paper uses Lyapunov–Krasovskii functionals in the stability analysis to ensure semi-global uniformly ultimately bounded tracking. The efficaciousness of the proposed CACC algorithm is demonstrated in simulation and through experimental implementation. Full article

(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)

► Show Figures

Figure 1

20 pages, 11395 KiB

Open AccessArticle

Autonomous Driving System Architecture with Integrated ROS2 and Adaptive AUTOSAR

by Dongwon Hong and Changjoo Moon

Electronics 2024, 13(7), 1303; https://doi.org/10.3390/electronics13071303 - 30 Mar 2024

Cited by 5 | Viewed by 7075

Abstract

In the automotive industry, research is now underway to apply Adaptive Automotive Open System Architecture (AUTOSAR) to the development of next-generation mobility, such as autonomous driving and connected cars. However, research on autonomous driving is being predominantly conducted on the robotics platform ROS2 (Robot Operating System 2). This demonstrates a considerable distance between autonomous driving research and its application in actual vehicles. To bridge this gap, interoperability that leverages the strengths of the Adaptive AUTOSAR and ROS2 platforms and compensates for their weaknesses is required. Therefore, this study proposes an architecture for interoperability between the two platforms, named Autonomous Driving System with Integrated ROS2 and Adaptive AUTOSAR (ASIRA). The proposed architecture enables communication between each of the two platforms through the ROS2 SOME/IP Bridge and allows for the necessary data exchange. It validates them in autonomous driving scenarios and goes beyond vehicle development, testing, and prototyping to exploit the advantages of each platform. Additionally, the simulation of autonomous vehicles within the ASIRA architecture is demonstrated by interoperating the ROS2 representative open-source autonomous driving project, Autoware, with the Adaptive AUTOSAR simulator. This study contributes to the assimilation of ROS2 into the automotive industry and its application in real vehicles by linking ROS2 and Adaptive AUTOSAR. Full article

(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)

► Show Figures

Figure 1

14 pages, 4720 KiB

Open AccessArticle

LiDAR Localization by Removing Moveable Objects

by Seonghark Jeong, Minseok Ko and Jungha Kim

Electronics 2023, 12(22), 4659; https://doi.org/10.3390/electronics12224659 - 15 Nov 2023

Cited by 6 | Viewed by 2468

Abstract

In this study, we propose reliable Light Detection and Ranging (LiDAR) mapping and localization via the removal of moveable objects, which can cause noise for autonomous driving vehicles based on the Normal Distributions Transform (NDT). LiDAR measures the distances to objects such as parked and moving cars and objects on the road, calculating the time of flight required for the sensor’s beam to reflect off an object and return to the system. The proposed localization system uses LiDAR to implement mapping and matching for the surroundings of an autonomous vehicle. This localization is applied to an autonomous vehicle, a mid-size Sports Utility Vehicle (SUV) that has a 64-channel Velodyne sensor, detecting moveable objects via modified DeepLabV3 and semantic segmentation. LiDAR and vision sensors are popular perception sensors, but vision sensors have a weakness that does not allow for an object to be detected accurately under special circumstances, such as at night or when there is a backlight in daylight. Even if LiDAR is more expensive than other detecting sensors, LiDAR can more reliably and accurately sense an object with the right depth because a LiDAR sensor estimates an object’s distance using the time of flight required for the LiDAR sensor’s beam to detect the object and return to the system. The cost of a LiDAR product will decrease dramatically in the case of skyrocketing demand for LiDAR in the industrial areas of autonomous vehicles, humanoid robots, service robots, and unmanned drones. As a result, this study develops a precise application of LiDAR localization for a mid-size SUV, which gives the best performance with respect to acquiring an object’s information and contributing to the appropriate, timely control of the mid-size SUV. We suggest mapping and localization using only LiDAR, without support from any other sensors, such as a Global Positioning System (GPS) or an Inertial Measurement Unit (IMU) sensor; using only a LiDAR sensor will be beneficial for cost competitiveness and reliability. With the powerful modified DeepLabV3, which is faster and more accurate, we identify and remove a moveable object through semantic segmentation. The improvement rate of the mapping and matching performance of our proposed NDT, by removing the moveable objects, was approximately 12% in terms of the Root-Mean-Square Error (RMSE) for the first fifth of the test course, where there were fewer parked cars and more moving cars. Full article

(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)

► Show Figures

Figure 1

Review

Jump to: Research

34 pages, 1007 KiB

Open AccessReview

Testing and Verification of Connected and Autonomous Vehicles: A Review

by Hayleyesus Alemayehu and Arman Sargolzaei

Electronics 2025, 14(3), 600; https://doi.org/10.3390/electronics14030600 - 3 Feb 2025

Cited by 1 | Viewed by 2627

Abstract

Connected and Autonomous Vehicles (CAVs) have the potential to revolutionize transportation by addressing critical challenges such as safety, energy efficiency, traffic congestion, and environmental impact. Realizing these benefits, however, requires the development of a rigorous testing and verification framework to enable the safe, efficient, and reliable deployment of CAVs across diverse operational scenarios. Despite the growing body of research, there remains a significant gap in review papers that comprehensively summarize recent studies related to the testing and verification of CAVs while identifying current challenges and highlighting future research directions. This paper seeks to address this gap by presenting a comprehensive review of the existing testing and verification frameworks for CAVs and identifying their associated challenges. Key topics covered include scenario generation, verification cost functions, assertion values, and security considerations. Furthermore, the paper highlights limitations within current frameworks, emphasizing the gaps that hinder systematic and comprehensive evaluations. Full article

(This article belongs to the Special Issue Advancements in Connected and Autonomous Vehicles)

► Show Figures

Journal Menu

Journal Browser

Advancements in Connected and Autonomous Vehicles

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI