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Electronics
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Innovations and Challenges in Automotive Mobility and Automation Systems
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Innovations and Challenges in Automotive Mobility and Automation Systems

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

Deadline for manuscript submissions: 25 October 2026 | Viewed by 15093

Special Issue Editors

Dr. Calin Iclodean

E-Mail Website
Guest Editor
Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania
Interests: electric vehicles; fuel cell vehicles; powertrain concept; electronic control unit; in-vehicle communication network; energy efficiency; autonomous vehicles; computer modeling and simulation in the automotive field
Special Issues, Collections and Topics in MDPI journals
Dr. Dan Moldovanu

E-Mail Website
Guest Editor
Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca Romania, 400114 Cluj-Napoca, Romania
Interests: system control; electric vehicles; hybrid vehicles; urban mobility; MATLAB

Special Issue Information

Dear Colleagues,

The Automotive Mobility, Management and Automation (AMMA 2025) Congress, hosted by the Technical University of Cluj-Napoca, Romania, on 23–25 October 2025, offers an opportunity to present recent technical and/or scientific developments.

This Special Issue aims to compile recent research and technological advancements in the fields of automotive mobility, intelligent management, and automation systems. With the rapid evolution of connected, autonomous, and electric vehicles, the automotive industry is undergoing a transformative shift. This Special Issue therefore welcomes original contributions that address innovative technologies, cutting-edge methodologies, and practical applications that are shaping the future of mobility. The scope of this Special Issue include, but is not limited to, advancements in autonomous driving, vehicle-to-everything (V2X) communication, electric and hydrogen-powered vehicles, intelligent transportation systems, sustainable mobility solutions, and the integration of AI and machine learning in vehicle systems.

The Special Issue also explores challenges related to the assurance of ensuring safety, reliability, and regulatory compliance in the deployment of these technologies. While it features selected contributions from the AMMA 2025 International Congress, we welcome submissions from all researchers and practitioners who are pushing the boundaries of automotive innovation. This platform aims to foster knowledge exchange and collaboration among academics, industry experts, and policymakers, contributing to the global transition towards smarter, greener, and safer mobility solutions.

We encourage scholars from diverse disciplines to share their insights and shape the future of the automotive industry. In this Special Issue, original research articles and reviews are welcome.

We look forward to receiving your contributions.

Dr. Calin Iclodean
Dr. Dan Moldovanu
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 250 words) can be sent to the Editorial Office for assessment.

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

  • advanced engineering
  • software and simulation
  • cybersecurity in automotive
  • green vehicle solutions
  • hybrid and electric vehicles
  • manufacturing technologies and materials
  • powertrain and propulsion
  • road safety
  • traffic management and transportation engineering

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

Published Papers (8 papers)

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Research

20 pages, 1488 KB  
Article
AI-Driven Hybrid Deep Learning and Swarm Intelligence for Predictive Maintenance of Smart Manufacturing Robots in Industry 4.0
by Deepak Kumar, Santosh Reddy Addula, Mary Lind, Steven Brown and Segun Odion
Electronics 2026, 15(3), 715; https://doi.org/10.3390/electronics15030715 - 6 Feb 2026
Cited by 4 | Viewed by 858
Abstract
Advancements in Industry 4.0 technologies, which combine big data analytics, robotics, and intelligent decision systems to enable new ways to increase automation in the industrial sector, have undergone significant transformations. In this research, a Hybrid Attention-Gated Recurrent Unit (At-GRU) model, combined with Sand [...] Read more.
Advancements in Industry 4.0 technologies, which combine big data analytics, robotics, and intelligent decision systems to enable new ways to increase automation in the industrial sector, have undergone significant transformations. In this research, a Hybrid Attention-Gated Recurrent Unit (At-GRU) model, combined with Sand Cat Optimization (SCO), is proposed to enhance fault identification and predictive maintenance capabilities. The model utilized multivariate sensor data from cyber-physical and IoT-enabled robotic platforms to learn operational patterns and predict failures with enhanced reliability. The At-GRU provides deeper temporal feature extraction, thereby improving classification performance. The robustness of the proposed model is validated through analysis of a benchmark dataset for industrial robots, and the results demonstrate that the proposed model exhibits impressive predictive capacity, surpassing other prediction methods and predictive maintenance approaches. Additionally, the performance evaluation indicates a lower computational cost due to the lightweight gating architecture of GRU, combined with attention. The robotic motion is further optimized by the SCO algorithm, which reduces energy usage, execution delay, and trajectory deviations while ensuring smooth operation. Overall, the proposed work offers an intelligent and scalable solution for next-generation industrial automation systems. Furthermore, the proposed model demonstrates the real-world applicability and significant benefits of incorporating hybrid artificial intelligence models into real-time robot control applications for smart manufacturing environments. Full article
(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)
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18 pages, 42966 KB  
Article
A Model-Based Design and Verification Framework for Virtual ECUs in Automotive Seat Control Systems
by Anna Yang, Woo Jin Han, Hyun Suk Cho, Dong-Woo Koh and Jae-Gon Kim
Electronics 2026, 15(3), 569; https://doi.org/10.3390/electronics15030569 - 28 Jan 2026
Viewed by 905
Abstract
As automotive software continues to grow in scale and timing sensitivity, hardware-independent verification in the early design phase has become increasingly important—especially for safety-critical, body-domain controllers. This study proposes a framework that integrates MBD (Model-Based Design), AUTOSAR (Automotive Open System Architecture) Classic Platform [...] Read more.
As automotive software continues to grow in scale and timing sensitivity, hardware-independent verification in the early design phase has become increasingly important—especially for safety-critical, body-domain controllers. This study proposes a framework that integrates MBD (Model-Based Design), AUTOSAR (Automotive Open System Architecture) Classic Platform configuration, and vECU (Virtual Electronic Control Unit) execution into a single, repeatable development workflow. Control logic validated in Simulink is translated into AUTOSAR-compliant software, built into a QEMU (Quick EMUlator)-based vECU, and exercised in DRIM-SimHub using both virtual stimuli and a real sensor–actuator signal delivered through a dedicated I/O interface board. Using a seat–slide virtual limit controller as a representative case, the proposed workflow enables consistent reuse of the test scenarios across model-in-the-loop (MiL), software-in-the-loop (SiL), and virtual ECU stages, while preserving production-level timing behavior and the semantics of the AUTOSAR runtime. The experimental results show that the vECU accurately reproduces the PWM outputs, Hall sensor pulse timing, and limit–stop decisions of physical ECU, and that integration issues previously discovered only in HiL tests can be exposed much earlier. Overall, the workflow shortens verification cycles, improves the observability of timing-dependent behavior, and provides a practical basis for early validation in software-defined vehicle development. Full article
(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)
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25 pages, 6809 KB  
Article
Sound Insulation Prediction and Analysis of Vehicle Floor Systems Based on Squeeze-and-Excitation ResNet Method
by Yan Ma, Jingjing Wang, Dianlong Pan, Wei Zhao, Xiaotao Yang, Xiaona Liu, Jie Yan and Weiping Ding
Electronics 2026, 15(1), 184; https://doi.org/10.3390/electronics15010184 - 30 Dec 2025
Viewed by 568
Abstract
The floor acoustic package is a crucial component of a vehicle’s overall acoustic insulation system, and its performance directly influences the interior sound field distribution and acoustic comfort. Conventional investigations of acoustic package performance primarily rely on experimental testing and computer-aided engineering (CAE) [...] Read more.
The floor acoustic package is a crucial component of a vehicle’s overall acoustic insulation system, and its performance directly influences the interior sound field distribution and acoustic comfort. Conventional investigations of acoustic package performance primarily rely on experimental testing and computer-aided engineering (CAE) simulations. However, these methods often suffer from limited accuracy control, high computational cost, and low efficiency. In contrast, data-driven modeling approaches have recently demonstrated strong potential in addressing these challenges. In this paper, a Squeeze-and-Excitation Residual Network (SE-ResNet) is proposed to predict and analyze the sound insulation performance of vehicle floor systems based on the original structural and material parameters of acoustic package components. By replacing the conventional CAE process with a data-driven framework, the proposed method enhances prediction accuracy and computational efficiency. With the lowest recorded RMSE of 0.4048 dB across the 200–8000 Hz spectrum, the SE-ResNet model ranks first in overall performance. It substantially outperforms the SE-CNN (0.9207 dB) and also shows a clear advantage over both the SE-LSTM (0.4591 dB) and the ResNet (0.4593 dB). Validation using the acoustic package data of a new vehicle model further confirms the robustness of the proposed approach, yielding an overall RMSE = 0.4089 dB and CORR = 0.9996 on the test dataset. These results collectively demonstrate that the SE-ResNet-based method presents a promising and robust solution for forecasting the sound insulation performance of vehicle floor systems. Moreover, the proposed framework offers methodological and technical support for the data-driven prediction and analysis of other vehicle noise and vibration problems. Full article
(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)
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17 pages, 1816 KB  
Article
Welcome to the Machine (WTTM): A Cybersecurity Framework for the Automotive Sector
by Enrico Picano and Massimo Fontana
Electronics 2025, 14(18), 3645; https://doi.org/10.3390/electronics14183645 - 15 Sep 2025
Viewed by 1544
Abstract
Cybersecurity has become a critical concern in the automotive sector, where the increasing connectivity and complexity of modern vehicles—particularly in the context of autonomous driving—have significantly expanded the attack surface. In response to these challenges, this paper presents the Welcome To The Machine [...] Read more.
Cybersecurity has become a critical concern in the automotive sector, where the increasing connectivity and complexity of modern vehicles—particularly in the context of autonomous driving—have significantly expanded the attack surface. In response to these challenges, this paper presents the Welcome To The Machine (WTTM) framework, developed to support proactive and structured cyber risk management throughout the entire vehicle lifecycle. Specifically tailored to the automotive domain, the framework encompasses four core actions: detection, analysis, response, and remediation. A central element of WTTM is the WTTM Questionnaire, designed to assess the organizational cybersecurity maturity of automotive manufacturers and suppliers. The questionnaire addresses six key areas: Governance, Risk Management, Concept and Design, Security Requirements, Validation and Testing, and Supply Chain. This paper focuses on the development and validation of WTTM-Q. Statistical validation was performed using responses from 43 participants, demonstrating high internal consistency (Cronbach’s alpha > 0.70) and strong construct validity (CFI = 0.94, RMSEA = 0.061). A supervised classifier (XGBoost), trained on 115 hypothetical response configurations, was employed to predict a priori risk classes, achieving 78% accuracy and a ROC AUC of 0.84. The WTTM framework, supported by a Vehicle Security Operations Center, provides a scalable, standards-aligned solution for enhancing cybersecurity in the automotive industry. Full article
(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)
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16 pages, 5561 KB  
Article
Smooth and Robust Path-Tracking Control for Automated Vehicles: From Theory to Real-World Applications
by Karin Festl, Selim Solmaz and Daniel Watzenig
Electronics 2025, 14(18), 3588; https://doi.org/10.3390/electronics14183588 - 10 Sep 2025
Cited by 1 | Viewed by 1199
Abstract
Path tracking is a fundamental challenge in the development of automated driving systems, requiring precise control of vehicle motion while ensuring smooth and stable actuation signals. Advancements in this field often lead to increasingly complex control solutions that demand significant computational effort and [...] Read more.
Path tracking is a fundamental challenge in the development of automated driving systems, requiring precise control of vehicle motion while ensuring smooth and stable actuation signals. Advancements in this field often lead to increasingly complex control solutions that demand significant computational effort and are difficult to parameterize. A novel variable structure path-tracking control approach that is based on the geometrically optimal solution of a Dubins car offers a promising solution to this challenge. The controller generates an n-smooth and differentially bounded steering angle, and with n + 1 parameters, it can be tuned towards performance, robustness, or low magnitude of the steering angle derivatives. In prior work, this controller demonstrated its performance, robustness, and tunablity in various simulations. In this contribution, we address the challenges of implementing this controller in a real vehicle, including system dead time, low sampling rates, and discontinuous paths. Key adaptations are proposed to ensure robust performance under these conditions. The controller is integrated into a comprehensive automated driving system, incorporating planning and velocity control, and evaluated during an overtaking maneuver (double-lane change) in a real-world setting. Experimental results show that the implemented controller successfully handles system dead time and path discontinuities, achieving consistent tracking errors of less than 0.3 m. Full article
(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)
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25 pages, 1078 KB  
Article
Road Accident Analysis and Prevention Using Autonomous Vehicles with Application for Montreal
by Manmeet Singh and Anjali Awasthi
Electronics 2025, 14(16), 3329; https://doi.org/10.3390/electronics14163329 - 21 Aug 2025
Cited by 1 | Viewed by 4213
Abstract
Road safety in cities is becoming a bigger concern worldwide. As more people own cars and traffic congestion increases on old roads, the risk of accidents also grows, which severely affects victims and their families. In 2023, data from the Société de l’Assurance [...] Read more.
Road safety in cities is becoming a bigger concern worldwide. As more people own cars and traffic congestion increases on old roads, the risk of accidents also grows, which severely affects victims and their families. In 2023, data from the Société de l’Assurance Automobile du Québec (SAAQ) reported that 380 people died in traffic accidents in Quebec. A study of road accidents in Montreal between 2012 and 2021 looked at the most dangerous locations, times, and traffic patterns. In this paper, we investigate the role of autonomous vehicles (AVs) vs human-driven vehicles (HDVs) in reducing road accidents in mixed traffic situations. The reaction time of human drivers to road accidents at signalized intersections affects safety and is used to compare the difference between the two situations. Microscopic traffic simulation models (MTMs) namely the Krauss car-following model is developed using SUMO to assess the vehicles performance. Case study 1 assesses the effect of reaction time on human-driven vehicles. The findings show that longer reaction times lead to more collisions. Case study 2 looks at autonomous vehicles and how human-driven vehicles interact in mixed traffic. The simulations tested various levels of AV penetration (0%, 25%, 50%, 75%, and 100%) in mixed traffic and found that more AVs on the road improve safety and reduce the number of accidents. Full article
(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)
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28 pages, 6342 KB  
Article
Optimizing the Energy Efficiency of Electric Vehicles in Urban and Metropolitan Environments According to Various Driving Cycles and Behavioral Conditions
by Călin-Doru Iclodean, Bogdan-Manolin Jurchis, Cristian-Marius Macavei, Edmond-Roland Volosciuc and Andrei-George Iclodean
Electronics 2025, 14(11), 2224; https://doi.org/10.3390/electronics14112224 - 29 May 2025
Viewed by 1881
Abstract
Electric vehicles are transforming urban and metropolitan transportation, providing significant benefits to both the environment and society. However, the integration of electric vehicles necessitates a well-planned infrastructure, including a sufficient number of charging stations distributed at the local level, policies that encourage the [...] Read more.
Electric vehicles are transforming urban and metropolitan transportation, providing significant benefits to both the environment and society. However, the integration of electric vehicles necessitates a well-planned infrastructure, including a sufficient number of charging stations distributed at the local level, policies that encourage the purchase and operation of electric vehicles, and the active participation of local governments and the automotive industry. Investments in improved car technologies, as well as renewable energy sources, will be critical in the shift to more sustainable metropolitan regions that have reduced pollution. Computer simulation based on virtual models performs an important role in the optimization of urban and metropolitan traffic by allowing for the rapid prototyping of real vehicle models, as well as the implementation of a wide range of test scenarios in real time. Assisted driving functions are critical in adjusting optimal driving behaviors to each of the particular scenarios of urban and metropolitan traffic. The situations discussed in this study were derived from real-world traffic and implemented and simulated on virtual models in the CarMaker version 12 application. To calibrate electricity consumption in each of the metropolitan area’s sectors, driving cycles were embedded in the virtual model. These were allocated to component sectors based on the average travel speed and its variation. Full article
(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)
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29 pages, 1326 KB  
Article
A Coordination Layer for Time Synchronization in Level-4 Multi-vECU Simulation
by Hyeongrae Kim, Harim Lee and Jeonghun Cho
Electronics 2025, 14(8), 1690; https://doi.org/10.3390/electronics14081690 - 21 Apr 2025
Cited by 3 | Viewed by 2360
Abstract
In automotive software development, testing and validation workloads are often concentrated at the end of the development cycle, leading to delays and late-stage issue discovery. To address this, virtual Electronic Control Units (vECUs) have gained attention for enabling earlier-stage verification. In our previous [...] Read more.
In automotive software development, testing and validation workloads are often concentrated at the end of the development cycle, leading to delays and late-stage issue discovery. To address this, virtual Electronic Control Units (vECUs) have gained attention for enabling earlier-stage verification. In our previous work, we developed a Level-4 vECU using a hardware-level emulator. However, when simulating multiple vECUs with independent clocks across distributed emulators, we observed poor timing reproducibility due to the lack of explicit synchronization. To solve this, we implemented an integration layer compliant with the functional mock-up interface (FMI), a widely used standard for simulation tool interoperability. The layer enables synchronized simulation between a centralized simulation master and independently running vECUs. We also developed a virtual CAN bus model to simulate message arbitration and validate inter-vECU communication behavior. Simulation results show that our framework correctly reproduces CAN arbitration logic and significantly improves timing reproducibility compared to conventional Linux-based interfaces. To improve simulation performance, the FMI master algorithm was parallelized, resulting in up to 85.2% reduction in simulation time with eight vECUs. These contributions offer a practical solution for synchronizing distributed Level-4 vECUs and lay the groundwork for future cloud-native simulation of automotive systems. Full article
(This article belongs to the Special Issue Innovations and Challenges in Automotive Mobility and Automation Systems)
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